IVOD_ID |
162626 |
IVOD_URL |
https://ivod.ly.gov.tw/Play/Clip/1M/162626 |
日期 |
2025-06-18 |
會議資料.會議代碼 |
委員會-11-3-26-17 |
會議資料.會議代碼:str |
第11屆第3會期社會福利及衛生環境委員會第17次全體委員會議 |
會議資料.屆 |
11 |
會議資料.會期 |
3 |
會議資料.會次 |
17 |
會議資料.種類 |
委員會 |
會議資料.委員會代碼[0] |
26 |
會議資料.委員會代碼:str[0] |
社會福利及衛生環境委員會 |
會議資料.標題 |
第11屆第3會期社會福利及衛生環境委員會第17次全體委員會議 |
影片種類 |
Clip |
開始時間 |
2025-06-18T11:48:41+08:00 |
結束時間 |
2025-06-18T12:09:28+08:00 |
影片長度 |
00:20:47 |
支援功能[0] |
ai-transcript |
video_url |
https://ivod-lyvod.cdn.hinet.net/vod_1/_definst_/mp4:1MClips/5617cf280bc2552a9cccf378e52ce464242af1fd96f58230a48dd392130a673609fd7804f724e0895ea18f28b6918d91.mp4/playlist.m3u8 |
委員名稱 |
林淑芬 |
委員發言時間 |
11:48:41 - 12:09:28 |
會議時間 |
2025-06-18T09:00:00+08:00 |
會議名稱 |
立法院第11屆第3會期社會福利及衛生環境委員會第17次全體委員會議(事由:邀請勞動部部長就「營造友善職場育兒環境,落實照顧不離職政策規劃」進行專題報告,並備質詢。) |
transcript.pyannote[0].speaker |
SPEAKER_01 |
transcript.pyannote[0].start |
0.03096875 |
transcript.pyannote[0].end |
1.02659375 |
transcript.pyannote[1].speaker |
SPEAKER_00 |
transcript.pyannote[1].start |
1.26284375 |
transcript.pyannote[1].end |
1.73534375 |
transcript.pyannote[2].speaker |
SPEAKER_00 |
transcript.pyannote[2].start |
10.49346875 |
transcript.pyannote[2].end |
11.82659375 |
transcript.pyannote[3].speaker |
SPEAKER_00 |
transcript.pyannote[3].start |
12.04596875 |
transcript.pyannote[3].end |
13.75034375 |
transcript.pyannote[4].speaker |
SPEAKER_00 |
transcript.pyannote[4].start |
13.86846875 |
transcript.pyannote[4].end |
28.27971875 |
transcript.pyannote[5].speaker |
SPEAKER_01 |
transcript.pyannote[5].start |
20.77034375 |
transcript.pyannote[5].end |
21.56346875 |
transcript.pyannote[6].speaker |
SPEAKER_01 |
transcript.pyannote[6].start |
33.62909375 |
transcript.pyannote[6].end |
34.21971875 |
transcript.pyannote[7].speaker |
SPEAKER_00 |
transcript.pyannote[7].start |
34.27034375 |
transcript.pyannote[7].end |
65.32034375 |
transcript.pyannote[8].speaker |
SPEAKER_01 |
transcript.pyannote[8].start |
67.42971875 |
transcript.pyannote[8].end |
67.93596875 |
transcript.pyannote[9].speaker |
SPEAKER_00 |
transcript.pyannote[9].start |
67.93596875 |
transcript.pyannote[9].end |
68.45909375 |
transcript.pyannote[10].speaker |
SPEAKER_00 |
transcript.pyannote[10].start |
69.69096875 |
transcript.pyannote[10].end |
72.12096875 |
transcript.pyannote[11].speaker |
SPEAKER_01 |
transcript.pyannote[11].start |
70.01159375 |
transcript.pyannote[11].end |
71.15909375 |
transcript.pyannote[12].speaker |
SPEAKER_01 |
transcript.pyannote[12].start |
72.61034375 |
transcript.pyannote[12].end |
75.91784375 |
transcript.pyannote[13].speaker |
SPEAKER_00 |
transcript.pyannote[13].start |
77.21721875 |
transcript.pyannote[13].end |
79.09034375 |
transcript.pyannote[14].speaker |
SPEAKER_00 |
transcript.pyannote[14].start |
79.74846875 |
transcript.pyannote[14].end |
85.21596875 |
transcript.pyannote[15].speaker |
SPEAKER_01 |
transcript.pyannote[15].start |
86.26221875 |
transcript.pyannote[15].end |
87.07221875 |
transcript.pyannote[16].speaker |
SPEAKER_01 |
transcript.pyannote[16].start |
87.10596875 |
transcript.pyannote[16].end |
88.84409375 |
transcript.pyannote[17].speaker |
SPEAKER_00 |
transcript.pyannote[17].start |
88.74284375 |
transcript.pyannote[17].end |
94.27784375 |
transcript.pyannote[18].speaker |
SPEAKER_01 |
transcript.pyannote[18].start |
91.27409375 |
transcript.pyannote[18].end |
91.81409375 |
transcript.pyannote[19].speaker |
SPEAKER_00 |
transcript.pyannote[19].start |
95.03721875 |
transcript.pyannote[19].end |
96.87659375 |
transcript.pyannote[20].speaker |
SPEAKER_01 |
transcript.pyannote[20].start |
96.04971875 |
transcript.pyannote[20].end |
101.07846875 |
transcript.pyannote[21].speaker |
SPEAKER_00 |
transcript.pyannote[21].start |
98.63159375 |
transcript.pyannote[21].end |
103.42409375 |
transcript.pyannote[22].speaker |
SPEAKER_00 |
transcript.pyannote[22].start |
104.40284375 |
transcript.pyannote[22].end |
113.02596875 |
transcript.pyannote[23].speaker |
SPEAKER_00 |
transcript.pyannote[23].start |
113.53221875 |
transcript.pyannote[23].end |
140.02596875 |
transcript.pyannote[24].speaker |
SPEAKER_00 |
transcript.pyannote[24].start |
140.09346875 |
transcript.pyannote[24].end |
142.48971875 |
transcript.pyannote[25].speaker |
SPEAKER_00 |
transcript.pyannote[25].start |
143.33346875 |
transcript.pyannote[25].end |
144.63284375 |
transcript.pyannote[26].speaker |
SPEAKER_01 |
transcript.pyannote[26].start |
144.63284375 |
transcript.pyannote[26].end |
146.10096875 |
transcript.pyannote[27].speaker |
SPEAKER_00 |
transcript.pyannote[27].start |
146.23596875 |
transcript.pyannote[27].end |
149.18909375 |
transcript.pyannote[28].speaker |
SPEAKER_00 |
transcript.pyannote[28].start |
149.74596875 |
transcript.pyannote[28].end |
182.09534375 |
transcript.pyannote[29].speaker |
SPEAKER_00 |
transcript.pyannote[29].start |
182.11221875 |
transcript.pyannote[29].end |
225.98721875 |
transcript.pyannote[30].speaker |
SPEAKER_00 |
transcript.pyannote[30].start |
226.57784375 |
transcript.pyannote[30].end |
238.91346875 |
transcript.pyannote[31].speaker |
SPEAKER_00 |
transcript.pyannote[31].start |
239.43659375 |
transcript.pyannote[31].end |
240.49971875 |
transcript.pyannote[32].speaker |
SPEAKER_00 |
transcript.pyannote[32].start |
240.78659375 |
transcript.pyannote[32].end |
267.90471875 |
transcript.pyannote[33].speaker |
SPEAKER_00 |
transcript.pyannote[33].start |
268.39409375 |
transcript.pyannote[33].end |
278.40096875 |
transcript.pyannote[34].speaker |
SPEAKER_00 |
transcript.pyannote[34].start |
278.68784375 |
transcript.pyannote[34].end |
294.78659375 |
transcript.pyannote[35].speaker |
SPEAKER_00 |
transcript.pyannote[35].start |
295.71471875 |
transcript.pyannote[35].end |
315.20534375 |
transcript.pyannote[36].speaker |
SPEAKER_00 |
transcript.pyannote[36].start |
315.34034375 |
transcript.pyannote[36].end |
318.09096875 |
transcript.pyannote[37].speaker |
SPEAKER_00 |
transcript.pyannote[37].start |
318.19221875 |
transcript.pyannote[37].end |
332.02971875 |
transcript.pyannote[38].speaker |
SPEAKER_00 |
transcript.pyannote[38].start |
332.29971875 |
transcript.pyannote[38].end |
336.85596875 |
transcript.pyannote[39].speaker |
SPEAKER_01 |
transcript.pyannote[39].start |
337.66596875 |
transcript.pyannote[39].end |
340.65284375 |
transcript.pyannote[40].speaker |
SPEAKER_00 |
transcript.pyannote[40].start |
341.69909375 |
transcript.pyannote[40].end |
343.31909375 |
transcript.pyannote[41].speaker |
SPEAKER_00 |
transcript.pyannote[41].start |
344.06159375 |
transcript.pyannote[41].end |
358.86096875 |
transcript.pyannote[42].speaker |
SPEAKER_00 |
transcript.pyannote[42].start |
359.13096875 |
transcript.pyannote[42].end |
359.73846875 |
transcript.pyannote[43].speaker |
SPEAKER_00 |
transcript.pyannote[43].start |
360.19409375 |
transcript.pyannote[43].end |
362.86034375 |
transcript.pyannote[44].speaker |
SPEAKER_00 |
transcript.pyannote[44].start |
363.23159375 |
transcript.pyannote[44].end |
365.12159375 |
transcript.pyannote[45].speaker |
SPEAKER_00 |
transcript.pyannote[45].start |
365.69534375 |
transcript.pyannote[45].end |
379.34721875 |
transcript.pyannote[46].speaker |
SPEAKER_00 |
transcript.pyannote[46].start |
379.66784375 |
transcript.pyannote[46].end |
401.67284375 |
transcript.pyannote[47].speaker |
SPEAKER_00 |
transcript.pyannote[47].start |
402.02721875 |
transcript.pyannote[47].end |
414.59909375 |
transcript.pyannote[48].speaker |
SPEAKER_00 |
transcript.pyannote[48].start |
415.44284375 |
transcript.pyannote[48].end |
421.63596875 |
transcript.pyannote[49].speaker |
SPEAKER_00 |
transcript.pyannote[49].start |
421.83846875 |
transcript.pyannote[49].end |
440.56971875 |
transcript.pyannote[50].speaker |
SPEAKER_00 |
transcript.pyannote[50].start |
441.10971875 |
transcript.pyannote[50].end |
444.67034375 |
transcript.pyannote[51].speaker |
SPEAKER_00 |
transcript.pyannote[51].start |
445.29471875 |
transcript.pyannote[51].end |
445.83471875 |
transcript.pyannote[52].speaker |
SPEAKER_00 |
transcript.pyannote[52].start |
446.79659375 |
transcript.pyannote[52].end |
449.80034375 |
transcript.pyannote[53].speaker |
SPEAKER_01 |
transcript.pyannote[53].start |
451.52159375 |
transcript.pyannote[53].end |
453.63096875 |
transcript.pyannote[54].speaker |
SPEAKER_01 |
transcript.pyannote[54].start |
453.79971875 |
transcript.pyannote[54].end |
454.71096875 |
transcript.pyannote[55].speaker |
SPEAKER_00 |
transcript.pyannote[55].start |
453.96846875 |
transcript.pyannote[55].end |
458.05221875 |
transcript.pyannote[56].speaker |
SPEAKER_01 |
transcript.pyannote[56].start |
459.26721875 |
transcript.pyannote[56].end |
459.89159375 |
transcript.pyannote[57].speaker |
SPEAKER_00 |
transcript.pyannote[57].start |
459.26721875 |
transcript.pyannote[57].end |
462.81096875 |
transcript.pyannote[58].speaker |
SPEAKER_01 |
transcript.pyannote[58].start |
462.89534375 |
transcript.pyannote[58].end |
472.96971875 |
transcript.pyannote[59].speaker |
SPEAKER_00 |
transcript.pyannote[59].start |
472.29471875 |
transcript.pyannote[59].end |
487.70159375 |
transcript.pyannote[60].speaker |
SPEAKER_00 |
transcript.pyannote[60].start |
487.83659375 |
transcript.pyannote[60].end |
493.45596875 |
transcript.pyannote[61].speaker |
SPEAKER_00 |
transcript.pyannote[61].start |
493.74284375 |
transcript.pyannote[61].end |
509.80784375 |
transcript.pyannote[62].speaker |
SPEAKER_00 |
transcript.pyannote[62].start |
510.76971875 |
transcript.pyannote[62].end |
511.10721875 |
transcript.pyannote[63].speaker |
SPEAKER_00 |
transcript.pyannote[63].start |
514.06034375 |
transcript.pyannote[63].end |
516.55784375 |
transcript.pyannote[64].speaker |
SPEAKER_00 |
transcript.pyannote[64].start |
517.51971875 |
transcript.pyannote[64].end |
520.10159375 |
transcript.pyannote[65].speaker |
SPEAKER_00 |
transcript.pyannote[65].start |
520.30409375 |
transcript.pyannote[65].end |
522.86909375 |
transcript.pyannote[66].speaker |
SPEAKER_00 |
transcript.pyannote[66].start |
523.56096875 |
transcript.pyannote[66].end |
525.21471875 |
transcript.pyannote[67].speaker |
SPEAKER_01 |
transcript.pyannote[67].start |
526.32846875 |
transcript.pyannote[67].end |
528.91034375 |
transcript.pyannote[68].speaker |
SPEAKER_00 |
transcript.pyannote[68].start |
527.49284375 |
transcript.pyannote[68].end |
527.91471875 |
transcript.pyannote[69].speaker |
SPEAKER_00 |
transcript.pyannote[69].start |
529.46721875 |
transcript.pyannote[69].end |
530.85096875 |
transcript.pyannote[70].speaker |
SPEAKER_01 |
transcript.pyannote[70].start |
530.58096875 |
transcript.pyannote[70].end |
531.01971875 |
transcript.pyannote[71].speaker |
SPEAKER_00 |
transcript.pyannote[71].start |
531.01971875 |
transcript.pyannote[71].end |
533.77034375 |
transcript.pyannote[72].speaker |
SPEAKER_00 |
transcript.pyannote[72].start |
535.15409375 |
transcript.pyannote[72].end |
535.50846875 |
transcript.pyannote[73].speaker |
SPEAKER_00 |
transcript.pyannote[73].start |
536.97659375 |
transcript.pyannote[73].end |
541.33034375 |
transcript.pyannote[74].speaker |
SPEAKER_01 |
transcript.pyannote[74].start |
541.33034375 |
transcript.pyannote[74].end |
541.83659375 |
transcript.pyannote[75].speaker |
SPEAKER_01 |
transcript.pyannote[75].start |
543.18659375 |
transcript.pyannote[75].end |
546.71346875 |
transcript.pyannote[76].speaker |
SPEAKER_00 |
transcript.pyannote[76].start |
546.30846875 |
transcript.pyannote[76].end |
549.19409375 |
transcript.pyannote[77].speaker |
SPEAKER_01 |
transcript.pyannote[77].start |
547.64159375 |
transcript.pyannote[77].end |
548.08034375 |
transcript.pyannote[78].speaker |
SPEAKER_01 |
transcript.pyannote[78].start |
548.53596875 |
transcript.pyannote[78].end |
563.03159375 |
transcript.pyannote[79].speaker |
SPEAKER_01 |
transcript.pyannote[79].start |
563.72346875 |
transcript.pyannote[79].end |
566.03534375 |
transcript.pyannote[80].speaker |
SPEAKER_00 |
transcript.pyannote[80].start |
566.03534375 |
transcript.pyannote[80].end |
566.13659375 |
transcript.pyannote[81].speaker |
SPEAKER_01 |
transcript.pyannote[81].start |
566.13659375 |
transcript.pyannote[81].end |
566.35596875 |
transcript.pyannote[82].speaker |
SPEAKER_00 |
transcript.pyannote[82].start |
566.35596875 |
transcript.pyannote[82].end |
569.08971875 |
transcript.pyannote[83].speaker |
SPEAKER_01 |
transcript.pyannote[83].start |
567.23346875 |
transcript.pyannote[83].end |
567.70596875 |
transcript.pyannote[84].speaker |
SPEAKER_00 |
transcript.pyannote[84].start |
569.41034375 |
transcript.pyannote[84].end |
570.20346875 |
transcript.pyannote[85].speaker |
SPEAKER_00 |
transcript.pyannote[85].start |
570.35534375 |
transcript.pyannote[85].end |
578.03346875 |
transcript.pyannote[86].speaker |
SPEAKER_00 |
transcript.pyannote[86].start |
578.38784375 |
transcript.pyannote[86].end |
580.22721875 |
transcript.pyannote[87].speaker |
SPEAKER_00 |
transcript.pyannote[87].start |
580.93596875 |
transcript.pyannote[87].end |
587.68596875 |
transcript.pyannote[88].speaker |
SPEAKER_00 |
transcript.pyannote[88].start |
588.46221875 |
transcript.pyannote[88].end |
591.65159375 |
transcript.pyannote[89].speaker |
SPEAKER_00 |
transcript.pyannote[89].start |
592.03971875 |
transcript.pyannote[89].end |
592.79909375 |
transcript.pyannote[90].speaker |
SPEAKER_00 |
transcript.pyannote[90].start |
594.08159375 |
transcript.pyannote[90].end |
597.03471875 |
transcript.pyannote[91].speaker |
SPEAKER_00 |
transcript.pyannote[91].start |
598.21596875 |
transcript.pyannote[91].end |
600.54471875 |
transcript.pyannote[92].speaker |
SPEAKER_00 |
transcript.pyannote[92].start |
601.38846875 |
transcript.pyannote[92].end |
602.62034375 |
transcript.pyannote[93].speaker |
SPEAKER_00 |
transcript.pyannote[93].start |
602.77221875 |
transcript.pyannote[93].end |
612.32346875 |
transcript.pyannote[94].speaker |
SPEAKER_00 |
transcript.pyannote[94].start |
612.49221875 |
transcript.pyannote[94].end |
614.04471875 |
transcript.pyannote[95].speaker |
SPEAKER_00 |
transcript.pyannote[95].start |
615.29346875 |
transcript.pyannote[95].end |
617.26784375 |
transcript.pyannote[96].speaker |
SPEAKER_00 |
transcript.pyannote[96].start |
618.09471875 |
transcript.pyannote[96].end |
619.32659375 |
transcript.pyannote[97].speaker |
SPEAKER_00 |
transcript.pyannote[97].start |
620.72721875 |
transcript.pyannote[97].end |
621.55409375 |
transcript.pyannote[98].speaker |
SPEAKER_00 |
transcript.pyannote[98].start |
622.85346875 |
transcript.pyannote[98].end |
625.62096875 |
transcript.pyannote[99].speaker |
SPEAKER_00 |
transcript.pyannote[99].start |
626.61659375 |
transcript.pyannote[99].end |
628.33784375 |
transcript.pyannote[100].speaker |
SPEAKER_00 |
transcript.pyannote[100].start |
628.69221875 |
transcript.pyannote[100].end |
638.73284375 |
transcript.pyannote[101].speaker |
SPEAKER_00 |
transcript.pyannote[101].start |
639.89721875 |
transcript.pyannote[101].end |
643.28909375 |
transcript.pyannote[102].speaker |
SPEAKER_00 |
transcript.pyannote[102].start |
644.35221875 |
transcript.pyannote[102].end |
676.56659375 |
transcript.pyannote[103].speaker |
SPEAKER_00 |
transcript.pyannote[103].start |
677.08971875 |
transcript.pyannote[103].end |
685.47659375 |
transcript.pyannote[104].speaker |
SPEAKER_00 |
transcript.pyannote[104].start |
686.15159375 |
transcript.pyannote[104].end |
694.52159375 |
transcript.pyannote[105].speaker |
SPEAKER_00 |
transcript.pyannote[105].start |
695.12909375 |
transcript.pyannote[105].end |
707.00909375 |
transcript.pyannote[106].speaker |
SPEAKER_00 |
transcript.pyannote[106].start |
707.05971875 |
transcript.pyannote[106].end |
716.94846875 |
transcript.pyannote[107].speaker |
SPEAKER_00 |
transcript.pyannote[107].start |
717.43784375 |
transcript.pyannote[107].end |
724.27221875 |
transcript.pyannote[108].speaker |
SPEAKER_00 |
transcript.pyannote[108].start |
724.40721875 |
transcript.pyannote[108].end |
725.40284375 |
transcript.pyannote[109].speaker |
SPEAKER_01 |
transcript.pyannote[109].start |
726.01034375 |
transcript.pyannote[109].end |
727.73159375 |
transcript.pyannote[110].speaker |
SPEAKER_00 |
transcript.pyannote[110].start |
727.73159375 |
transcript.pyannote[110].end |
728.32221875 |
transcript.pyannote[111].speaker |
SPEAKER_00 |
transcript.pyannote[111].start |
728.59221875 |
transcript.pyannote[111].end |
730.38096875 |
transcript.pyannote[112].speaker |
SPEAKER_00 |
transcript.pyannote[112].start |
730.66784375 |
transcript.pyannote[112].end |
734.14409375 |
transcript.pyannote[113].speaker |
SPEAKER_00 |
transcript.pyannote[113].start |
734.38034375 |
transcript.pyannote[113].end |
740.06721875 |
transcript.pyannote[114].speaker |
SPEAKER_00 |
transcript.pyannote[114].start |
740.40471875 |
transcript.pyannote[114].end |
748.35284375 |
transcript.pyannote[115].speaker |
SPEAKER_00 |
transcript.pyannote[115].start |
748.94346875 |
transcript.pyannote[115].end |
751.57596875 |
transcript.pyannote[116].speaker |
SPEAKER_00 |
transcript.pyannote[116].start |
751.82909375 |
transcript.pyannote[116].end |
758.96721875 |
transcript.pyannote[117].speaker |
SPEAKER_00 |
transcript.pyannote[117].start |
759.67596875 |
transcript.pyannote[117].end |
765.19409375 |
transcript.pyannote[118].speaker |
SPEAKER_01 |
transcript.pyannote[118].start |
766.47659375 |
transcript.pyannote[118].end |
769.58159375 |
transcript.pyannote[119].speaker |
SPEAKER_00 |
transcript.pyannote[119].start |
769.58159375 |
transcript.pyannote[119].end |
773.56409375 |
transcript.pyannote[120].speaker |
SPEAKER_00 |
transcript.pyannote[120].start |
774.25596875 |
transcript.pyannote[120].end |
776.01096875 |
transcript.pyannote[121].speaker |
SPEAKER_00 |
transcript.pyannote[121].start |
776.90534375 |
transcript.pyannote[121].end |
790.27034375 |
transcript.pyannote[122].speaker |
SPEAKER_00 |
transcript.pyannote[122].start |
790.47284375 |
transcript.pyannote[122].end |
797.61096875 |
transcript.pyannote[123].speaker |
SPEAKER_00 |
transcript.pyannote[123].start |
798.96096875 |
transcript.pyannote[123].end |
800.12534375 |
transcript.pyannote[124].speaker |
SPEAKER_00 |
transcript.pyannote[124].start |
800.71596875 |
transcript.pyannote[124].end |
801.40784375 |
transcript.pyannote[125].speaker |
SPEAKER_00 |
transcript.pyannote[125].start |
801.94784375 |
transcript.pyannote[125].end |
802.89284375 |
transcript.pyannote[126].speaker |
SPEAKER_00 |
transcript.pyannote[126].start |
803.17971875 |
transcript.pyannote[126].end |
804.83346875 |
transcript.pyannote[127].speaker |
SPEAKER_00 |
transcript.pyannote[127].start |
805.98096875 |
transcript.pyannote[127].end |
808.22534375 |
transcript.pyannote[128].speaker |
SPEAKER_00 |
transcript.pyannote[128].start |
808.51221875 |
transcript.pyannote[128].end |
814.50284375 |
transcript.pyannote[129].speaker |
SPEAKER_01 |
transcript.pyannote[129].start |
808.66409375 |
transcript.pyannote[129].end |
808.90034375 |
transcript.pyannote[130].speaker |
SPEAKER_00 |
transcript.pyannote[130].start |
814.58721875 |
transcript.pyannote[130].end |
817.08471875 |
transcript.pyannote[131].speaker |
SPEAKER_00 |
transcript.pyannote[131].start |
817.99596875 |
transcript.pyannote[131].end |
818.77221875 |
transcript.pyannote[132].speaker |
SPEAKER_00 |
transcript.pyannote[132].start |
818.87346875 |
transcript.pyannote[132].end |
820.12221875 |
transcript.pyannote[133].speaker |
SPEAKER_00 |
transcript.pyannote[133].start |
820.62846875 |
transcript.pyannote[133].end |
827.56409375 |
transcript.pyannote[134].speaker |
SPEAKER_00 |
transcript.pyannote[134].start |
828.59346875 |
transcript.pyannote[134].end |
829.28534375 |
transcript.pyannote[135].speaker |
SPEAKER_00 |
transcript.pyannote[135].start |
830.23034375 |
transcript.pyannote[135].end |
843.84846875 |
transcript.pyannote[136].speaker |
SPEAKER_00 |
transcript.pyannote[136].start |
843.89909375 |
transcript.pyannote[136].end |
843.91596875 |
transcript.pyannote[137].speaker |
SPEAKER_00 |
transcript.pyannote[137].start |
843.96659375 |
transcript.pyannote[137].end |
848.43846875 |
transcript.pyannote[138].speaker |
SPEAKER_00 |
transcript.pyannote[138].start |
848.94471875 |
transcript.pyannote[138].end |
870.98346875 |
transcript.pyannote[139].speaker |
SPEAKER_00 |
transcript.pyannote[139].start |
872.06346875 |
transcript.pyannote[139].end |
883.42034375 |
transcript.pyannote[140].speaker |
SPEAKER_00 |
transcript.pyannote[140].start |
883.74096875 |
transcript.pyannote[140].end |
884.90534375 |
transcript.pyannote[141].speaker |
SPEAKER_00 |
transcript.pyannote[141].start |
885.98534375 |
transcript.pyannote[141].end |
889.61346875 |
transcript.pyannote[142].speaker |
SPEAKER_00 |
transcript.pyannote[142].start |
890.17034375 |
transcript.pyannote[142].end |
897.98346875 |
transcript.pyannote[143].speaker |
SPEAKER_00 |
transcript.pyannote[143].start |
898.70909375 |
transcript.pyannote[143].end |
904.36221875 |
transcript.pyannote[144].speaker |
SPEAKER_00 |
transcript.pyannote[144].start |
904.81784375 |
transcript.pyannote[144].end |
907.83846875 |
transcript.pyannote[145].speaker |
SPEAKER_00 |
transcript.pyannote[145].start |
908.83409375 |
transcript.pyannote[145].end |
909.81284375 |
transcript.pyannote[146].speaker |
SPEAKER_00 |
transcript.pyannote[146].start |
910.25159375 |
transcript.pyannote[146].end |
912.10784375 |
transcript.pyannote[147].speaker |
SPEAKER_00 |
transcript.pyannote[147].start |
912.64784375 |
transcript.pyannote[147].end |
915.38159375 |
transcript.pyannote[148].speaker |
SPEAKER_00 |
transcript.pyannote[148].start |
915.75284375 |
transcript.pyannote[148].end |
917.50784375 |
transcript.pyannote[149].speaker |
SPEAKER_00 |
transcript.pyannote[149].start |
919.97159375 |
transcript.pyannote[149].end |
932.50971875 |
transcript.pyannote[150].speaker |
SPEAKER_00 |
transcript.pyannote[150].start |
932.84721875 |
transcript.pyannote[150].end |
935.26034375 |
transcript.pyannote[151].speaker |
SPEAKER_00 |
transcript.pyannote[151].start |
936.00284375 |
transcript.pyannote[151].end |
936.99846875 |
transcript.pyannote[152].speaker |
SPEAKER_00 |
transcript.pyannote[152].start |
937.77471875 |
transcript.pyannote[152].end |
945.19971875 |
transcript.pyannote[153].speaker |
SPEAKER_00 |
transcript.pyannote[153].start |
945.58784375 |
transcript.pyannote[153].end |
947.17409375 |
transcript.pyannote[154].speaker |
SPEAKER_00 |
transcript.pyannote[154].start |
947.96721875 |
transcript.pyannote[154].end |
954.02534375 |
transcript.pyannote[155].speaker |
SPEAKER_00 |
transcript.pyannote[155].start |
954.46409375 |
transcript.pyannote[155].end |
962.90159375 |
transcript.pyannote[156].speaker |
SPEAKER_00 |
transcript.pyannote[156].start |
963.07034375 |
transcript.pyannote[156].end |
963.98159375 |
transcript.pyannote[157].speaker |
SPEAKER_00 |
transcript.pyannote[157].start |
964.33596875 |
transcript.pyannote[157].end |
966.73221875 |
transcript.pyannote[158].speaker |
SPEAKER_00 |
transcript.pyannote[158].start |
967.62659375 |
transcript.pyannote[158].end |
970.86659375 |
transcript.pyannote[159].speaker |
SPEAKER_00 |
transcript.pyannote[159].start |
971.35596875 |
transcript.pyannote[159].end |
972.43596875 |
transcript.pyannote[160].speaker |
SPEAKER_00 |
transcript.pyannote[160].start |
972.84096875 |
transcript.pyannote[160].end |
975.47346875 |
transcript.pyannote[161].speaker |
SPEAKER_00 |
transcript.pyannote[161].start |
975.82784375 |
transcript.pyannote[161].end |
976.97534375 |
transcript.pyannote[162].speaker |
SPEAKER_00 |
transcript.pyannote[162].start |
977.48159375 |
transcript.pyannote[162].end |
978.49409375 |
transcript.pyannote[163].speaker |
SPEAKER_01 |
transcript.pyannote[163].start |
981.43034375 |
transcript.pyannote[163].end |
984.61971875 |
transcript.pyannote[164].speaker |
SPEAKER_01 |
transcript.pyannote[164].start |
984.97409375 |
transcript.pyannote[164].end |
986.89784375 |
transcript.pyannote[165].speaker |
SPEAKER_01 |
transcript.pyannote[165].start |
987.48846875 |
transcript.pyannote[165].end |
996.09471875 |
transcript.pyannote[166].speaker |
SPEAKER_00 |
transcript.pyannote[166].start |
994.13721875 |
transcript.pyannote[166].end |
996.22971875 |
transcript.pyannote[167].speaker |
SPEAKER_00 |
transcript.pyannote[167].start |
996.39846875 |
transcript.pyannote[167].end |
1001.24159375 |
transcript.pyannote[168].speaker |
SPEAKER_00 |
transcript.pyannote[168].start |
1001.88284375 |
transcript.pyannote[168].end |
1007.94096875 |
transcript.pyannote[169].speaker |
SPEAKER_00 |
transcript.pyannote[169].start |
1008.27846875 |
transcript.pyannote[169].end |
1012.32846875 |
transcript.pyannote[170].speaker |
SPEAKER_01 |
transcript.pyannote[170].start |
1012.96971875 |
transcript.pyannote[170].end |
1013.59409375 |
transcript.pyannote[171].speaker |
SPEAKER_00 |
transcript.pyannote[171].start |
1013.27346875 |
transcript.pyannote[171].end |
1014.72471875 |
transcript.pyannote[172].speaker |
SPEAKER_00 |
transcript.pyannote[172].start |
1017.08721875 |
transcript.pyannote[172].end |
1017.71159375 |
transcript.pyannote[173].speaker |
SPEAKER_00 |
transcript.pyannote[173].start |
1017.96471875 |
transcript.pyannote[173].end |
1021.22159375 |
transcript.pyannote[174].speaker |
SPEAKER_00 |
transcript.pyannote[174].start |
1021.45784375 |
transcript.pyannote[174].end |
1024.42784375 |
transcript.pyannote[175].speaker |
SPEAKER_00 |
transcript.pyannote[175].start |
1024.79909375 |
transcript.pyannote[175].end |
1034.11409375 |
transcript.pyannote[176].speaker |
SPEAKER_00 |
transcript.pyannote[176].start |
1034.56971875 |
transcript.pyannote[176].end |
1041.26909375 |
transcript.pyannote[177].speaker |
SPEAKER_00 |
transcript.pyannote[177].start |
1041.72471875 |
transcript.pyannote[177].end |
1049.16659375 |
transcript.pyannote[178].speaker |
SPEAKER_00 |
transcript.pyannote[178].start |
1050.07784375 |
transcript.pyannote[178].end |
1060.55721875 |
transcript.pyannote[179].speaker |
SPEAKER_00 |
transcript.pyannote[179].start |
1061.43471875 |
transcript.pyannote[179].end |
1073.11221875 |
transcript.pyannote[180].speaker |
SPEAKER_00 |
transcript.pyannote[180].start |
1073.58471875 |
transcript.pyannote[180].end |
1084.89096875 |
transcript.pyannote[181].speaker |
SPEAKER_00 |
transcript.pyannote[181].start |
1085.44784375 |
transcript.pyannote[181].end |
1087.25346875 |
transcript.pyannote[182].speaker |
SPEAKER_00 |
transcript.pyannote[182].start |
1087.89471875 |
transcript.pyannote[182].end |
1093.15971875 |
transcript.pyannote[183].speaker |
SPEAKER_00 |
transcript.pyannote[183].start |
1093.56471875 |
transcript.pyannote[183].end |
1097.78346875 |
transcript.pyannote[184].speaker |
SPEAKER_00 |
transcript.pyannote[184].start |
1098.22221875 |
transcript.pyannote[184].end |
1101.47909375 |
transcript.pyannote[185].speaker |
SPEAKER_00 |
transcript.pyannote[185].start |
1102.30596875 |
transcript.pyannote[185].end |
1111.16534375 |
transcript.pyannote[186].speaker |
SPEAKER_00 |
transcript.pyannote[186].start |
1111.40159375 |
transcript.pyannote[186].end |
1119.02909375 |
transcript.pyannote[187].speaker |
SPEAKER_00 |
transcript.pyannote[187].start |
1119.38346875 |
transcript.pyannote[187].end |
1122.26909375 |
transcript.pyannote[188].speaker |
SPEAKER_00 |
transcript.pyannote[188].start |
1122.47159375 |
transcript.pyannote[188].end |
1129.72784375 |
transcript.pyannote[189].speaker |
SPEAKER_00 |
transcript.pyannote[189].start |
1130.50409375 |
transcript.pyannote[189].end |
1132.44471875 |
transcript.pyannote[190].speaker |
SPEAKER_00 |
transcript.pyannote[190].start |
1132.83284375 |
transcript.pyannote[190].end |
1144.45971875 |
transcript.pyannote[191].speaker |
SPEAKER_00 |
transcript.pyannote[191].start |
1145.10096875 |
transcript.pyannote[191].end |
1145.57346875 |
transcript.pyannote[192].speaker |
SPEAKER_00 |
transcript.pyannote[192].start |
1145.84346875 |
transcript.pyannote[192].end |
1147.91909375 |
transcript.pyannote[193].speaker |
SPEAKER_01 |
transcript.pyannote[193].start |
1147.91909375 |
transcript.pyannote[193].end |
1148.74596875 |
transcript.pyannote[194].speaker |
SPEAKER_01 |
transcript.pyannote[194].start |
1149.11721875 |
transcript.pyannote[194].end |
1155.22596875 |
transcript.pyannote[195].speaker |
SPEAKER_01 |
transcript.pyannote[195].start |
1155.61409375 |
transcript.pyannote[195].end |
1156.12034375 |
transcript.pyannote[196].speaker |
SPEAKER_01 |
transcript.pyannote[196].start |
1157.03159375 |
transcript.pyannote[196].end |
1158.29721875 |
transcript.pyannote[197].speaker |
SPEAKER_01 |
transcript.pyannote[197].start |
1158.73596875 |
transcript.pyannote[197].end |
1165.75596875 |
transcript.pyannote[198].speaker |
SPEAKER_00 |
transcript.pyannote[198].start |
1165.58721875 |
transcript.pyannote[198].end |
1167.64596875 |
transcript.pyannote[199].speaker |
SPEAKER_00 |
transcript.pyannote[199].start |
1169.60346875 |
transcript.pyannote[199].end |
1170.63284375 |
transcript.pyannote[200].speaker |
SPEAKER_01 |
transcript.pyannote[200].start |
1171.34159375 |
transcript.pyannote[200].end |
1172.23596875 |
transcript.pyannote[201].speaker |
SPEAKER_00 |
transcript.pyannote[201].start |
1171.98284375 |
transcript.pyannote[201].end |
1174.26096875 |
transcript.pyannote[202].speaker |
SPEAKER_00 |
transcript.pyannote[202].start |
1174.56471875 |
transcript.pyannote[202].end |
1176.37034375 |
transcript.pyannote[203].speaker |
SPEAKER_00 |
transcript.pyannote[203].start |
1177.70346875 |
transcript.pyannote[203].end |
1179.91409375 |
transcript.pyannote[204].speaker |
SPEAKER_00 |
transcript.pyannote[204].start |
1179.96471875 |
transcript.pyannote[204].end |
1181.36534375 |
transcript.pyannote[205].speaker |
SPEAKER_01 |
transcript.pyannote[205].start |
1181.36534375 |
transcript.pyannote[205].end |
1181.70284375 |
transcript.pyannote[206].speaker |
SPEAKER_00 |
transcript.pyannote[206].start |
1181.70284375 |
transcript.pyannote[206].end |
1188.03096875 |
transcript.pyannote[207].speaker |
SPEAKER_00 |
transcript.pyannote[207].start |
1188.21659375 |
transcript.pyannote[207].end |
1197.83534375 |
transcript.pyannote[208].speaker |
SPEAKER_00 |
transcript.pyannote[208].start |
1199.16846875 |
transcript.pyannote[208].end |
1206.28971875 |
transcript.pyannote[209].speaker |
SPEAKER_00 |
transcript.pyannote[209].start |
1206.55971875 |
transcript.pyannote[209].end |
1211.09909375 |
transcript.pyannote[210].speaker |
SPEAKER_00 |
transcript.pyannote[210].start |
1212.01034375 |
transcript.pyannote[210].end |
1220.34659375 |
transcript.pyannote[211].speaker |
SPEAKER_00 |
transcript.pyannote[211].start |
1220.97096875 |
transcript.pyannote[211].end |
1225.30784375 |
transcript.pyannote[212].speaker |
SPEAKER_00 |
transcript.pyannote[212].start |
1225.98284375 |
transcript.pyannote[212].end |
1227.99096875 |
transcript.pyannote[213].speaker |
SPEAKER_00 |
transcript.pyannote[213].start |
1229.37471875 |
transcript.pyannote[213].end |
1231.02846875 |
transcript.pyannote[214].speaker |
SPEAKER_00 |
transcript.pyannote[214].start |
1231.73721875 |
transcript.pyannote[214].end |
1232.59784375 |
transcript.pyannote[215].speaker |
SPEAKER_00 |
transcript.pyannote[215].start |
1233.12096875 |
transcript.pyannote[215].end |
1233.50909375 |
transcript.pyannote[216].speaker |
SPEAKER_00 |
transcript.pyannote[216].start |
1234.92659375 |
transcript.pyannote[216].end |
1235.31471875 |
transcript.pyannote[217].speaker |
SPEAKER_00 |
transcript.pyannote[217].start |
1235.50034375 |
transcript.pyannote[217].end |
1237.98096875 |
transcript.pyannote[218].speaker |
SPEAKER_00 |
transcript.pyannote[218].start |
1238.18346875 |
transcript.pyannote[218].end |
1242.65534375 |
transcript.pyannote[219].speaker |
SPEAKER_00 |
transcript.pyannote[219].start |
1242.95909375 |
transcript.pyannote[219].end |
1243.73534375 |
transcript.whisperx[0].start |
0.029 |
transcript.whisperx[0].end |
2.231 |
transcript.whisperx[0].text |
好 謝謝主席喔我再去邀功一下這個我們的警員在國道上面配備有緩衝車標配是我要求的是我提案的 以前都沒有好 那個這個跟今天無關那我們是不是還是請洪部長 |
transcript.whisperx[1].start |
34.426 |
transcript.whisperx[1].end |
50.713 |
transcript.whisperx[1].text |
部長營造友善的職場育兒環境要落實照顧不離職的政策那我現在要問你第一個問題你知道現在什麼都漲的時代只有剩下人力和社畜的薪水最便宜都不太漲 |
transcript.whisperx[2].start |
51.713 |
transcript.whisperx[2].end |
65.085 |
transcript.whisperx[2].text |
但生養小孩啊我要問你第一個問題連教育基金都是年年增加你知道養一個小孩大概要多少的教育基金嗎教育基金就好了那如果你不知道的話你知道嗎我可能有人會抓高一點有人抓低一點吧 |
transcript.whisperx[3].start |
77.267 |
transcript.whisperx[3].end |
102.359 |
transcript.whisperx[3].text |
你講這個不是空話嗎我現在顯然你不知道教育金要準備多少那你這樣養一個小孩要多少錢嗎這有高一點的跟低一點的部長你就說你不知道就好了你為什麼要這樣子胡扯呢都跟你講剛剛講空話有些人不是胡扯啊就是有些人有些小孩有些人高一點有些人低一點你叫阿嬤或狗來講也會講 |
transcript.whisperx[4].start |
104.434 |
transcript.whisperx[4].end |
111.856 |
transcript.whisperx[4].text |
我現在跟你講喔 那你要大概一個概念嘛 你不能讓人家說你吃飯卡中 寶釘兩枚 四個孩子要開多少錢 都不知道所以我們 我也是很訝異啊 因為我個人覺得沒花這麼多但是呢 還是有人講喔 那如果有一個民間的保存人壽他們去做2024年的教育金準備大調查 因為準備嘛 可能從 |
transcript.whisperx[5].start |
129.921 |
transcript.whisperx[5].end |
142.205 |
transcript.whisperx[5].text |
幼兒園一路準備到他要出國留學如果到家的出國留學我肯定不是這個數字啊你知道如果要準備小孩出國留學一年要多少錢嗎到美國留學一年要花多少錢到英國要多少錢可能兩三百以上 |
transcript.whisperx[6].start |
146.543 |
transcript.whisperx[6].end |
160.996 |
transcript.whisperx[6].text |
兩三百以上那不可能啊不夠一年不夠那我們就不講出國留學就是說從小的幼兒園到大學畢業那他們的調查顯示每位子女的教育準備金在2023年是459萬那2024年增加了22萬要481萬而且有30% |
transcript.whisperx[7].start |
173.267 |
transcript.whisperx[7].end |
189.841 |
transcript.whisperx[7].text |
8%的受訪者認為需要準備到500萬元以上如果你要留學一年還要再增加400萬元一年啦至少啦到美國400萬跑不掉除非他拿公費補助除非他留學的時候說我 |
transcript.whisperx[8].start |
191.002 |
transcript.whisperx[8].end |
196.723 |
transcript.whisperx[8].text |
我要去打工這樣子所以我們今天要討論一個營造友善職場育兒環境之前我們希望我們的政府要了解為什麼現在的年輕人不想婚婚了以後不想生因為生一個小孩光是教育成本要為他準備的就要這麼多在這種狀況裡面我們希望說照顧不離職 |
transcript.whisperx[9].start |
219.127 |
transcript.whisperx[9].end |
221.349 |
transcript.whisperx[9].text |
不能離職,可是如果你沒有足夠的配套給他支持的話,事實上他就只能選擇不生 |
transcript.whisperx[10].start |
239.46 |
transcript.whisperx[10].end |
264.019 |
transcript.whisperx[10].text |
不生啊所以我們來講營造友善職場的育兒環境前提是他願意生他生了以後我們需要這個國家來介入所以為什麼現在女性啊她絕對不會想說我沒有獨立的經濟經濟考量是影響生育決策的很重要的一個因素那我在這裡跟你講其實經濟負擔最大的還是房子房貸 |
transcript.whisperx[11].start |
268.442 |
transcript.whisperx[11].end |
294.421 |
transcript.whisperx[11].text |
所以請你回去行政院跟他們講願意婚生的婦女願意婚生的年輕夫妻要打造一條龍式的然後打五折的社會住宅社會住宅裡面就有幼兒園就有托嬰中心然後甚至還有老人家長輩的安置的日照 |
transcript.whisperx[12].start |
296.108 |
transcript.whisperx[12].end |
314.709 |
transcript.whisperx[12].text |
這樣子一條龍才能夠真正的鼓勵人家來婚生所以這我要打造一個讓家長安心生養的環境很重要那我們知道職業不中斷呢除了我剛剛講最務實的一條龍式的打五折的社會住宅以外 |
transcript.whisperx[13].start |
315.61 |
transcript.whisperx[13].end |
336.774 |
transcript.whisperx[13].text |
當然是勞動環境勞動條件而我們檢視一個政府做的好不好啊很簡單大家講拿出來講兩個指標一個是出生率一個指標就是勞參率勞動參與率出生率出生率部長你知道現在出生率是怎麼一回事啊現在出生率是是很低的每況愈下 |
transcript.whisperx[14].start |
344.113 |
transcript.whisperx[14].end |
352.264 |
transcript.whisperx[14].text |
每況愈下我說過古尼龍尼龍年生的小孩應該會多有史以來專呆玩的狼專呆玩的狼從來長眼睛沒見過龍年出生的小孩逼氣骨厚頭爪 |
transcript.whisperx[15].start |
363.354 |
transcript.whisperx[15].end |
378.079 |
transcript.whisperx[15].text |
比他們還要低這是一個非常大的警訊我們要講說如果不講出生率檢視你友善又育兒政策的另外一個指標就是勞參率 |
transcript.whisperx[16].start |
379.719 |
transcript.whisperx[16].end |
389.667 |
transcript.whisperx[16].text |
那根據112年勞動部的性別勞動統計表分析男女勞動的參與率的差距這10年裡面他本來差距了16.28%現在下降到15.23%顯然看起來好像女性的勞參率多了1% |
transcript.whisperx[17].start |
402.316 |
transcript.whisperx[17].end |
413.972 |
transcript.whisperx[17].text |
看起來好像有進步那等一下再來講可是我們還有一個看有配偶或同居的男性勞參率有65.12相對的有配偶或同居的女性勞參率 |
transcript.whisperx[18].start |
415.492 |
transcript.whisperx[18].end |
428.362 |
transcript.whisperx[18].text |
女郎還是49.34差了15.78個百分點那如果再講單離婚的分居的上偶的變成單親的這個單身的男性勞參率51.96然後女性 |
transcript.whisperx[19].start |
434.966 |
transcript.whisperx[19].end |
449.754 |
transcript.whisperx[19].text |
上偶的離婚的分居的女性的勞參率30.18%遠遠男生高出了21.78%這個到底是怎麼一回事啊部長你覺得呢目前現在看起來確實單身的男性的勞參率遠遠高於女性 |
transcript.whisperx[20].start |
459.329 |
transcript.whisperx[20].end |
479.03 |
transcript.whisperx[20].text |
比一般平均的男女的勞參率還要更高目前看到確實女性的勞參率在20歲以前其實相對是維持著很不錯的可是30歲以後就會快速的蠻快速的降低我不是問你這個問題我是問你離婚分居和上偶老實說因為我想不懂因為這是你們統計數字嘛 |
transcript.whisperx[21].start |
480.552 |
transcript.whisperx[21].end |
509.346 |
transcript.whisperx[21].text |
如果這些你們的統計數字那顯然是有意義的為什麼離婚分居尚偶的性別的勞參率勞參率的這個性別的差異會擴大為什麼你們這個統計的人你們自己要出來給我們解說啊但我要問你啦如果我們去思考說從這裡面的男女性的勞參率的數字這裡面有子女的人有沒有差異有沒有差異 |
transcript.whisperx[22].start |
514.117 |
transcript.whisperx[22].end |
533.613 |
transcript.whisperx[22].text |
不然你們做這個統計是在做什麼心臟的你們的統計單位做這間統計的人你們在做什麼異議的意思是什麼你們自己在做你們不知道甘委員你是問句嗎對我在問你們啊你們的性別勞動統計分析報告的啊蛤 |
transcript.whisperx[23].start |
537.028 |
transcript.whisperx[23].end |
562.807 |
transcript.whisperx[23].text |
你們自己做統計分析你們不知道你們做這個變數的意義是什麼因為你剛才講的好幾個數據我要講離婚分居上我們現在確實是看到我覺得很明顯的事情是女性的勞參率低於男性一個很大的原因是因為他必須要從自己的職場給離開去做家庭的照顧不管是顧小或顧老 |
transcript.whisperx[24].start |
563.803 |
transcript.whisperx[24].end |
587.32 |
transcript.whisperx[24].text |
都會涵蓋在這裡面所以這也是這個我們大家都知道啦但是我要跟你講我剛剛講2002到2020抱歉102年到112年看起來女性勞參率好像上升對不對可是不要忘囉不要忘囉102年到112年女性勞參率上升的同時女性的生育率是下降的小孩的出生人口數是下降的這有沒有意義 |
transcript.whisperx[25].start |
594.127 |
transcript.whisperx[25].end |
596.808 |
transcript.whisperx[25].text |
不要看得好像你們勞參與增加很開心很開心不生小孩啦所以這是什麼意思這是說所有的女性有工作在職場的女性還是必須在工作跟生小孩之間做抉擇啊只能擇一啊如果你要工作可能你就會選擇不生小孩 |
transcript.whisperx[26].start |
623.018 |
transcript.whisperx[26].end |
625.2 |
transcript.whisperx[26].text |
這一趴顯示的有這個現象啊那為什麼這樣子所以在這裡大家都在講說 |
transcript.whisperx[27].start |
645.184 |
transcript.whisperx[27].end |
654.009 |
transcript.whisperx[27].text |
104銀行也有講 因為剛才林月琴委員有諮詢29歲以下他的職業來中斷 職場的瓶頸是什麼29歲以下就跟人家煩惱要生孩子 會中斷嗎30到39歲當中有58%是白天上班 晚上顧家 疲於奔命 影響工作表現或身心俱疲還有一個也是生孩子 |
transcript.whisperx[28].start |
670.819 |
transcript.whisperx[28].end |
693.091 |
transcript.whisperx[28].text |
40到49歲將近六成也是白天上班晚上顧家疲於奔命影響工作表現或俱疲到50歲以上67%就是職業中斷以後想重返職場因年紀大或專業過時而無法銜接所以這個裡面其實大家顯示出來都很累很累大家不分年紀就是很累很累很累 |
transcript.whisperx[29].start |
695.202 |
transcript.whisperx[29].end |
712.739 |
transcript.whisperx[29].text |
無論顧小或顧老 其實就是要這樣子 就是這樣子那我們現在在講說 玩意心的 玩意心的 讓他們友善一點 國家做一點什麼國家做一點什麼 我舉一個例子 你知道為什麼需要彈性輕職價嗎我再問你自己的問題啦 你知道 |
transcript.whisperx[30].start |
717.523 |
transcript.whisperx[30].end |
723.427 |
transcript.whisperx[30].text |
台北市去年的托嬰中心因為腸病毒而停課的天數是幾天嗎 |
transcript.whisperx[31].start |
726.032 |
transcript.whisperx[31].end |
747.481 |
transcript.whisperx[31].text |
大概20天出頭我告訴你平均公立的托嬰中心19.9天私立的托嬰中心平均一年停課18.9天那我要舉喔這個台北市有這個幾大行政區域嘛松山區公立的平均一年停課天數 |
transcript.whisperx[32].start |
749.863 |
transcript.whisperx[32].end |
775.64 |
transcript.whisperx[32].text |
28天28天私立平均一年停課天數在南港區也是28天28天你知道一個腸病毒就這麼多天那你知道六都其他五都的平均多少天嗎就我看到的數據是比台北市低一些新北市平均一年停課11天高雄公立 |
transcript.whisperx[33].start |
776.983 |
transcript.whisperx[33].end |
789.825 |
transcript.whisperx[33].text |
新北市統計數字比較粗糙啦但高雄公立的平均一年停課天數25天私立的一年平均停課天數22天請問你 請問你 保定我們要去哪每一個勞工 女性勞工年輕父母要去哪 28天 25天 22天啊我們要請什麼假 請特休特休是什麼 特休是讓這個勞工 |
transcript.whisperx[34].start |
806.284 |
transcript.whisperx[34].end |
816.254 |
transcript.whisperx[34].text |
幾年來身心俱肥讓他能休息一下現在大家特殊都不是拿來自己充電再出發的大家特殊都拿來照顧孩子因為不要講這個空中的大便有新家庭照顧這麼不可能那一年滾下去不可能的啦所以 |
transcript.whisperx[35].start |
830.273 |
transcript.whisperx[35].end |
845.222 |
transcript.whisperx[35].text |
不但如此 政府沒有配套然後沒有作為就算了 現在還落井下石雪上加霜你知道要做什麼嗎政府的政策跨部會之間都不協調為什麼不協調 你知道嗎利益良善缺乏配套加重家長的負擔變成不敢生今年4月我們衛福部修正了 預告了 |
transcript.whisperx[36].start |
856.989 |
transcript.whisperx[36].end |
870.22 |
transcript.whisperx[36].text |
托嬰中心定型化契約應記載及不得記載事項新增加了一款如果兒童健康不佳時托嬰中心有權要求家長配合接回照顧 |
transcript.whisperx[37].start |
872.353 |
transcript.whisperx[37].end |
882.5 |
transcript.whisperx[37].text |
這個修正方向其實是好的 我覺得是好的自己小孩生病 趕快自己接回去照顧這也是基於公共衛生防疫需求 利益良善啦但是我問你 只藏病毒的28天20天啊小孩健康不佳 我這有經驗了小孩剛從保姆到幼兒園的時候 一個月整天都在生病 |
transcript.whisperx[38].start |
898.77 |
transcript.whisperx[38].end |
907.616 |
transcript.whisperx[38].text |
健康不佳托嬰中心就有權利要求家長接回去自己照顧啊我要去吃架出來保電我問你啦沒有配套然後你要保護孩子沒有錯這些家長要去哪裡長出那個架啦要去哪裡長出來啦 |
transcript.whisperx[39].start |
920.037 |
transcript.whisperx[39].end |
944.569 |
transcript.whisperx[39].text |
所以就算透映中心的停課規定放寬了每年的停課日數有可能下降到平均只有十多天但是因為你們定型化契約這樣子一改下去所以大家家長都要很緊繃的本來是大家都覺得說願意升了結果法律上沒有配套就說你就要坐等 家長都要坐等這大家就不要死啊 |
transcript.whisperx[40].start |
948.055 |
transcript.whisperx[40].end |
966.428 |
transcript.whisperx[40].text |
雪上加霜你們在有生育意願的這個年輕父母身上雪上加霜啊讓他們情何以堪啊所以我剛剛講說特休假本來是為了工作及生活平衡結果因為生了孩子以後特休假 |
transcript.whisperx[41].start |
967.674 |
transcript.whisperx[41].end |
976.862 |
transcript.whisperx[41].text |
不要忘了 年輕父母的特休假天數最少因為之前嘛 結果特休假全部要用來照顧小孩情何以堪啊 寶釘 你看勒 |
transcript.whisperx[42].start |
981.965 |
transcript.whisperx[42].end |
995.87 |
transcript.whisperx[42].text |
所以這部分我們為什麼正在研議這個雲流亭希望能夠更多更時間更短的可以讓大家可以請休的原因就是希望要去補足這部分你講的很有道理啊我們的法案躺在那裡已經躺兩三年了躺好幾年了啦 |
transcript.whisperx[43].start |
1001.889 |
transcript.whisperx[43].end |
1011.773 |
transcript.whisperx[43].text |
但是呢 我問你 你的現憑工作平等法你的就業保險法 你的修法你說就是要 那請問你什麼時候要拿出來修什麼時候要排審查 張偉什麼時候要排審查 |
transcript.whisperx[44].start |
1017.39 |
transcript.whisperx[44].end |
1020.492 |
transcript.whisperx[44].text |
對啊 我沒有增加天數 沒有讓僱主多付錢欸我把育嬰留庭的那幾天育嬰留庭增加一個月啦那我們再把這一個月從0到3歲給他放到0到8歲或是0到12歲然後呢 請假的方法 不同說限制1個月1年1個月到2年你都通通給他鬆綁掉 彈性化了 |
transcript.whisperx[45].start |
1044.656 |
transcript.whisperx[45].end |
1066.947 |
transcript.whisperx[45].text |
沒有增加天數欸沒有增加這個錢欸這種狀況你都沒有辦法做然後你說你就是靠這個配套要來幫家長解決問題可是你口惠實不至連法案都沒有拿出來審我們已經提案多久了都沒有提出來審我上一屆就提 大家上一屆都提了提到這一屆還繼續提好 因為你叫我時間到了我現在跟你說 國家做了什麼 |
transcript.whisperx[46].start |
1073.969 |
transcript.whisperx[46].end |
1100.821 |
transcript.whisperx[46].text |
國家做了什麼 沒有所以年輕世代這個年輕世代我告訴大家沒有人會為了生兒育女而放棄工作沒有人如果這個要育兒而且不要放棄工作的話只能很辛苦很辛苦的在那裡撐著大家都想要繼續工作大家都想要繼續賺錢養家大家都覺得經濟很困難 |
transcript.whisperx[47].start |
1102.695 |
transcript.whisperx[47].end |
1129.049 |
transcript.whisperx[47].text |
這個是事實所以在這種狀況裡面女人要照顧小的然後到40歲左右又要照顧老的在這種狀況裡面在身心俱疲而你能做的政策面你能端出來的有限啊我告訴你台姓親職價這種最簡單的你今天說一件事都說你自己你的法案沒有排審 |
transcript.whisperx[48].start |
1130.578 |
transcript.whisperx[48].end |
1155.704 |
transcript.whisperx[48].text |
法案都沒有排審欸提案提多久了什麼時候要拿出來審什麼時候要通過這才是真正的真正的落實你的口會沒有實質啊對不對保定我講這個沒道理那個有根文說我們前面階段我們會先從在可以不修法的範圍內我們會先做那 |
transcript.whisperx[49].start |
1157.222 |
transcript.whisperx[49].end |
1167.626 |
transcript.whisperx[49].text |
以日請休或者是這幾相關的談心話目前可以做我覺得我們會在先第一段先來這部分我們會來還是往這個方向我們要修法你支不支持問的就簡單咧我們的價沒有增加天數我們的錢沒有增加 |
transcript.whisperx[50].start |
1177.905 |
transcript.whisperx[50].end |
1191.355 |
transcript.whisperx[50].text |
當然是雇主的行政成本 它是增加的就是這樣子 如果錢跟天數都沒有增加了這樣子你們都沒有辦法協調我告訴你國安危機內 去年13萬 今年22萬阿拉美礦獄下你留步先倒 全國大家一起倒 |
transcript.whisperx[51].start |
1199.689 |
transcript.whisperx[51].end |
1224.821 |
transcript.whisperx[51].text |
事情很嚴重到現在我們還不願意正視這個國家沒有人要生孩子這個國家國家的政策端不出友善育兒的制度來誰願意生啊我再告訴你我再說一遍年輕人沒有人會放棄工作因為我們都必須工作才有飯吃沒有人要放棄工作只能放棄不生小孩只能放棄生小孩 |
transcript.whisperx[52].start |
1229.397 |
transcript.whisperx[52].end |
1229.817 |
transcript.whisperx[52].text |
這件事很嚴重 到現在你們還在拚拚拚拚拚拚拚拚拚拚拚 |