iVOD / 169784

Field Value
IVOD_ID 169784
IVOD_URL https://ivod.ly.gov.tw/Play/Clip/1M/169784
日期 2026-06-03
會議資料.會議代碼 委員會-11-5-26-13
會議資料.會議代碼:str 第11屆第5會期社會福利及衛生環境委員會第13次全體委員會議
會議資料.屆 11
會議資料.會期 5
會議資料.會次 13
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5會期社會福利及衛生環境委員會第13次全體委員會議
影片種類 Clip
開始時間 2026-06-03T11:32:12+08:00
結束時間 2026-06-03T11:44:08+08:00
影片長度 00:11:56
支援功能[0] ai-transcript
video_url https://ivod-lyvod.cdn.hinet.net/vod_1/_definst_/mp4:1MClips/5329b193fad04125da3d71ba5fe7ebcb5efbe1c17d8a019977c83729728f4ea830a324e446e41dda5ea18f28b6918d91.mp4/playlist.m3u8
委員名稱 蘇清泉
委員發言時間 11:32:12 - 11:44:08
會議時間 2026-06-03T09:00:00+08:00
會議名稱 立法院第11屆第5會期社會福利及衛生環境委員會第13次全體委員會議(事由:(6月3日上午9時起) 一、審查「性別平等工作法」修正草案等100案。 (一)委員范雲等17人擬具「性別平等工作法部分條文修正草案」案。 (二)委員郭昱晴等17人擬具「性別平等工作法第十五條條文修正草案」案。 (三)委員萬美玲等35人擬具「性別平等工作法第十五條條文修正草案」案。 (四)委員黃秀芳等18人擬具「性別平等工作法第十五條條文修正草案」案。 (五)委員黃健豪等21人擬具「性別平等工作法第二十條條文修正草案」案。 (六)委員黃健豪等21人擬具「性別平等工作法第十九條之一及第二十一條條文修正草案」案。 (七)委員李彥秀等22人擬具「性別平等工作法第十五條條文修正草案」案。 (八)委員許智傑等25人擬具「性別平等工作法第二十條條文修正草案」案。 (九)委員吳宗憲等16人擬具「性別平等工作法第十四條條文修正草案」案。 (十)委員吳宗憲等17人擬具「性別平等工作法第十五條條文修正草案」案。 ......(因系統字數上限,詳見議事日程) 二、審查「就業保險法」修正草案等88案。 (一)委員謝衣鳯等19人擬具「就業保險法第十條、第十一條及第十九條之三條文修正草案」案。 (二)委員范雲等17人擬具「就業保險法第十一條及第十九條之二條文修正草案」案。 (三)委員許宇甄等22人擬具「就業保險法第十一條條文修正草案」案。 (四)委員邱鎮軍等20人擬具「就業保險法第十一條條文修正草案」案。 (五)委員謝衣鳯等17人擬具「就業保險法第十四條條文修正草案」案。 (六)委員黃健豪等20人擬具「就業保險法第十條、第十一條及第十九條之三條文修正草案」案。 (七)委員林淑芬等23人擬具「就業保險法第十一條及第十九條之三條文修正草案」案。 (八)委員徐欣瑩等20人擬具「就業保險法第十九條之二條文修正草案」案。 (九)委員涂權吉等16人擬具「就業保險法第十條、第十一條及第十九條之二條文修正草案」案。 (十)委員王育敏等18人擬具「就業保險法部分條文修正草案」案。 ......(因系統字數上限,詳見議事日程) 【綜合詢答,僅詢答】 【第一(一○○)案及第二(八十八)案,如經復議則不予審查】 (6月3日下午2時30分起) (6月3日若上午議程尚未結束,待結束後接續召開) 一、繼續審查中華民國115年度中央政府總預算案關於勞動部主管預算。(公務及非營業特種基金預算案)。 二、繼續審查勞動部函送財團法人職業災害預防及重建中心115年度預算書案。 【6月3日、6月4日二天一次會】)
transcript.pyannote[0].speaker SPEAKER_01
transcript.pyannote[0].start 6.61221875
transcript.pyannote[0].end 14.96534375
transcript.pyannote[1].speaker SPEAKER_00
transcript.pyannote[1].start 9.81846875
transcript.pyannote[1].end 9.86909375
transcript.pyannote[2].speaker SPEAKER_00
transcript.pyannote[2].start 10.54409375
transcript.pyannote[2].end 10.78034375
transcript.pyannote[3].speaker SPEAKER_00
transcript.pyannote[3].start 14.34096875
transcript.pyannote[3].end 15.89346875
transcript.pyannote[4].speaker SPEAKER_01
transcript.pyannote[4].start 19.52159375
transcript.pyannote[4].end 22.55909375
transcript.pyannote[5].speaker SPEAKER_00
transcript.pyannote[5].start 19.97721875
transcript.pyannote[5].end 20.66909375
transcript.pyannote[6].speaker SPEAKER_01
transcript.pyannote[6].start 24.02721875
transcript.pyannote[6].end 25.32659375
transcript.pyannote[7].speaker SPEAKER_01
transcript.pyannote[7].start 25.88346875
transcript.pyannote[7].end 30.91221875
transcript.pyannote[8].speaker SPEAKER_01
transcript.pyannote[8].start 31.33409375
transcript.pyannote[8].end 39.13034375
transcript.pyannote[9].speaker SPEAKER_01
transcript.pyannote[9].start 40.64909375
transcript.pyannote[9].end 42.84284375
transcript.pyannote[10].speaker SPEAKER_00
transcript.pyannote[10].start 41.15534375
transcript.pyannote[10].end 41.56034375
transcript.pyannote[11].speaker SPEAKER_01
transcript.pyannote[11].start 43.46721875
transcript.pyannote[11].end 47.41596875
transcript.pyannote[12].speaker SPEAKER_01
transcript.pyannote[12].start 47.73659375
transcript.pyannote[12].end 48.46221875
transcript.pyannote[13].speaker SPEAKER_01
transcript.pyannote[13].start 49.30596875
transcript.pyannote[13].end 50.48721875
transcript.pyannote[14].speaker SPEAKER_01
transcript.pyannote[14].start 51.46596875
transcript.pyannote[14].end 56.41034375
transcript.pyannote[15].speaker SPEAKER_01
transcript.pyannote[15].start 57.57471875
transcript.pyannote[15].end 59.70096875
transcript.pyannote[16].speaker SPEAKER_01
transcript.pyannote[16].start 60.81471875
transcript.pyannote[16].end 67.91909375
transcript.pyannote[17].speaker SPEAKER_01
transcript.pyannote[17].start 68.49284375
transcript.pyannote[17].end 70.45034375
transcript.pyannote[18].speaker SPEAKER_01
transcript.pyannote[18].start 71.05784375
transcript.pyannote[18].end 72.74534375
transcript.pyannote[19].speaker SPEAKER_01
transcript.pyannote[19].start 73.77471875
transcript.pyannote[19].end 84.18659375
transcript.pyannote[20].speaker SPEAKER_01
transcript.pyannote[20].start 84.33846875
transcript.pyannote[20].end 86.90346875
transcript.pyannote[21].speaker SPEAKER_01
transcript.pyannote[21].start 87.76409375
transcript.pyannote[21].end 95.34096875
transcript.pyannote[22].speaker SPEAKER_01
transcript.pyannote[22].start 95.71221875
transcript.pyannote[22].end 102.52971875
transcript.pyannote[23].speaker SPEAKER_01
transcript.pyannote[23].start 103.60971875
transcript.pyannote[23].end 109.75221875
transcript.pyannote[24].speaker SPEAKER_01
transcript.pyannote[24].start 111.10221875
transcript.pyannote[24].end 114.81471875
transcript.pyannote[25].speaker SPEAKER_01
transcript.pyannote[25].start 115.55721875
transcript.pyannote[25].end 121.02471875
transcript.pyannote[26].speaker SPEAKER_00
transcript.pyannote[26].start 121.02471875
transcript.pyannote[26].end 129.19221875
transcript.pyannote[27].speaker SPEAKER_01
transcript.pyannote[27].start 129.19221875
transcript.pyannote[27].end 129.20909375
transcript.pyannote[28].speaker SPEAKER_00
transcript.pyannote[28].start 130.59284375
transcript.pyannote[28].end 130.96409375
transcript.pyannote[29].speaker SPEAKER_01
transcript.pyannote[29].start 130.96409375
transcript.pyannote[29].end 146.94471875
transcript.pyannote[30].speaker SPEAKER_00
transcript.pyannote[30].start 131.36909375
transcript.pyannote[30].end 132.29721875
transcript.pyannote[31].speaker SPEAKER_00
transcript.pyannote[31].start 133.14096875
transcript.pyannote[31].end 134.17034375
transcript.pyannote[32].speaker SPEAKER_01
transcript.pyannote[32].start 147.23159375
transcript.pyannote[32].end 152.85096875
transcript.pyannote[33].speaker SPEAKER_01
transcript.pyannote[33].start 153.05346875
transcript.pyannote[33].end 155.11221875
transcript.pyannote[34].speaker SPEAKER_01
transcript.pyannote[34].start 158.41971875
transcript.pyannote[34].end 159.49971875
transcript.pyannote[35].speaker SPEAKER_01
transcript.pyannote[35].start 160.07346875
transcript.pyannote[35].end 161.25471875
transcript.pyannote[36].speaker SPEAKER_00
transcript.pyannote[36].start 165.38909375
transcript.pyannote[36].end 173.26971875
transcript.pyannote[37].speaker SPEAKER_00
transcript.pyannote[37].start 174.02909375
transcript.pyannote[37].end 178.36596875
transcript.pyannote[38].speaker SPEAKER_00
transcript.pyannote[38].start 178.99034375
transcript.pyannote[38].end 182.98971875
transcript.pyannote[39].speaker SPEAKER_00
transcript.pyannote[39].start 183.56346875
transcript.pyannote[39].end 184.47471875
transcript.pyannote[40].speaker SPEAKER_00
transcript.pyannote[40].start 185.06534375
transcript.pyannote[40].end 196.50659375
transcript.pyannote[41].speaker SPEAKER_01
transcript.pyannote[41].start 194.66721875
transcript.pyannote[41].end 194.78534375
transcript.pyannote[42].speaker SPEAKER_01
transcript.pyannote[42].start 195.40971875
transcript.pyannote[42].end 204.31971875
transcript.pyannote[43].speaker SPEAKER_01
transcript.pyannote[43].start 204.92721875
transcript.pyannote[43].end 210.76596875
transcript.pyannote[44].speaker SPEAKER_00
transcript.pyannote[44].start 209.77034375
transcript.pyannote[44].end 213.38159375
transcript.pyannote[45].speaker SPEAKER_01
transcript.pyannote[45].start 212.95971875
transcript.pyannote[45].end 218.66346875
transcript.pyannote[46].speaker SPEAKER_01
transcript.pyannote[46].start 218.95034375
transcript.pyannote[46].end 220.26659375
transcript.pyannote[47].speaker SPEAKER_01
transcript.pyannote[47].start 220.50284375
transcript.pyannote[47].end 222.08909375
transcript.pyannote[48].speaker SPEAKER_01
transcript.pyannote[48].start 222.37596875
transcript.pyannote[48].end 223.42221875
transcript.pyannote[49].speaker SPEAKER_01
transcript.pyannote[49].start 224.06346875
transcript.pyannote[49].end 225.63284375
transcript.pyannote[50].speaker SPEAKER_01
transcript.pyannote[50].start 225.98721875
transcript.pyannote[50].end 230.17221875
transcript.pyannote[51].speaker SPEAKER_01
transcript.pyannote[51].start 230.72909375
transcript.pyannote[51].end 233.26034375
transcript.pyannote[52].speaker SPEAKER_01
transcript.pyannote[52].start 233.85096875
transcript.pyannote[52].end 234.37409375
transcript.pyannote[53].speaker SPEAKER_01
transcript.pyannote[53].start 235.58909375
transcript.pyannote[53].end 237.49596875
transcript.pyannote[54].speaker SPEAKER_01
transcript.pyannote[54].start 239.26784375
transcript.pyannote[54].end 243.52034375
transcript.pyannote[55].speaker SPEAKER_01
transcript.pyannote[55].start 243.84096875
transcript.pyannote[55].end 248.75159375
transcript.pyannote[56].speaker SPEAKER_01
transcript.pyannote[56].start 249.03846875
transcript.pyannote[56].end 256.58159375
transcript.pyannote[57].speaker SPEAKER_01
transcript.pyannote[57].start 257.18909375
transcript.pyannote[57].end 261.39096875
transcript.pyannote[58].speaker SPEAKER_01
transcript.pyannote[58].start 261.71159375
transcript.pyannote[58].end 263.53409375
transcript.pyannote[59].speaker SPEAKER_01
transcript.pyannote[59].start 264.02346875
transcript.pyannote[59].end 265.50846875
transcript.pyannote[60].speaker SPEAKER_01
transcript.pyannote[60].start 266.13284375
transcript.pyannote[60].end 273.22034375
transcript.pyannote[61].speaker SPEAKER_01
transcript.pyannote[61].start 273.84471875
transcript.pyannote[61].end 277.13534375
transcript.pyannote[62].speaker SPEAKER_01
transcript.pyannote[62].start 277.59096875
transcript.pyannote[62].end 284.30721875
transcript.pyannote[63].speaker SPEAKER_01
transcript.pyannote[63].start 284.71221875
transcript.pyannote[63].end 291.73221875
transcript.pyannote[64].speaker SPEAKER_01
transcript.pyannote[64].start 292.10346875
transcript.pyannote[64].end 292.79534375
transcript.pyannote[65].speaker SPEAKER_01
transcript.pyannote[65].start 293.14971875
transcript.pyannote[65].end 298.16159375
transcript.pyannote[66].speaker SPEAKER_01
transcript.pyannote[66].start 299.69721875
transcript.pyannote[66].end 304.89471875
transcript.pyannote[67].speaker SPEAKER_00
transcript.pyannote[67].start 299.74784375
transcript.pyannote[67].end 300.42284375
transcript.pyannote[68].speaker SPEAKER_01
transcript.pyannote[68].start 305.18159375
transcript.pyannote[68].end 305.68784375
transcript.pyannote[69].speaker SPEAKER_01
transcript.pyannote[69].start 306.22784375
transcript.pyannote[69].end 307.35846875
transcript.pyannote[70].speaker SPEAKER_01
transcript.pyannote[70].start 310.26096875
transcript.pyannote[70].end 314.26034375
transcript.pyannote[71].speaker SPEAKER_01
transcript.pyannote[71].start 315.13784375
transcript.pyannote[71].end 316.45409375
transcript.pyannote[72].speaker SPEAKER_01
transcript.pyannote[72].start 317.01096875
transcript.pyannote[72].end 317.56784375
transcript.pyannote[73].speaker SPEAKER_01
transcript.pyannote[73].start 317.78721875
transcript.pyannote[73].end 319.01909375
transcript.pyannote[74].speaker SPEAKER_00
transcript.pyannote[74].start 317.92221875
transcript.pyannote[74].end 318.07409375
transcript.pyannote[75].speaker SPEAKER_01
transcript.pyannote[75].start 319.32284375
transcript.pyannote[75].end 319.84596875
transcript.pyannote[76].speaker SPEAKER_01
transcript.pyannote[76].start 321.26346875
transcript.pyannote[76].end 323.22096875
transcript.pyannote[77].speaker SPEAKER_01
transcript.pyannote[77].start 323.96346875
transcript.pyannote[77].end 324.75659375
transcript.pyannote[78].speaker SPEAKER_00
transcript.pyannote[78].start 324.03096875
transcript.pyannote[78].end 324.68909375
transcript.pyannote[79].speaker SPEAKER_00
transcript.pyannote[79].start 324.75659375
transcript.pyannote[79].end 324.97596875
transcript.pyannote[80].speaker SPEAKER_01
transcript.pyannote[80].start 324.97596875
transcript.pyannote[80].end 327.38909375
transcript.pyannote[81].speaker SPEAKER_01
transcript.pyannote[81].start 329.32971875
transcript.pyannote[81].end 331.32096875
transcript.pyannote[82].speaker SPEAKER_01
transcript.pyannote[82].start 332.36721875
transcript.pyannote[82].end 333.51471875
transcript.pyannote[83].speaker SPEAKER_01
transcript.pyannote[83].start 334.25721875
transcript.pyannote[83].end 338.32409375
transcript.pyannote[84].speaker SPEAKER_01
transcript.pyannote[84].start 338.50971875
transcript.pyannote[84].end 341.02409375
transcript.pyannote[85].speaker SPEAKER_00
transcript.pyannote[85].start 341.02409375
transcript.pyannote[85].end 341.63159375
transcript.pyannote[86].speaker SPEAKER_01
transcript.pyannote[86].start 341.63159375
transcript.pyannote[86].end 341.71596875
transcript.pyannote[87].speaker SPEAKER_01
transcript.pyannote[87].start 342.07034375
transcript.pyannote[87].end 342.08721875
transcript.pyannote[88].speaker SPEAKER_00
transcript.pyannote[88].start 342.08721875
transcript.pyannote[88].end 345.29346875
transcript.pyannote[89].speaker SPEAKER_00
transcript.pyannote[89].start 346.23846875
transcript.pyannote[89].end 346.89659375
transcript.pyannote[90].speaker SPEAKER_00
transcript.pyannote[90].start 347.94284375
transcript.pyannote[90].end 355.03034375
transcript.pyannote[91].speaker SPEAKER_01
transcript.pyannote[91].start 352.07721875
transcript.pyannote[91].end 352.38096875
transcript.pyannote[92].speaker SPEAKER_00
transcript.pyannote[92].start 355.72221875
transcript.pyannote[92].end 383.93721875
transcript.pyannote[93].speaker SPEAKER_01
transcript.pyannote[93].start 363.45096875
transcript.pyannote[93].end 364.29471875
transcript.pyannote[94].speaker SPEAKER_00
transcript.pyannote[94].start 384.86534375
transcript.pyannote[94].end 385.20284375
transcript.pyannote[95].speaker SPEAKER_00
transcript.pyannote[95].start 386.56971875
transcript.pyannote[95].end 399.64784375
transcript.pyannote[96].speaker SPEAKER_00
transcript.pyannote[96].start 399.83346875
transcript.pyannote[96].end 403.20846875
transcript.pyannote[97].speaker SPEAKER_01
transcript.pyannote[97].start 399.96846875
transcript.pyannote[97].end 400.25534375
transcript.pyannote[98].speaker SPEAKER_01
transcript.pyannote[98].start 400.45784375
transcript.pyannote[98].end 401.50409375
transcript.pyannote[99].speaker SPEAKER_01
transcript.pyannote[99].start 401.74034375
transcript.pyannote[99].end 403.12409375
transcript.pyannote[100].speaker SPEAKER_00
transcript.pyannote[100].start 403.39409375
transcript.pyannote[100].end 404.13659375
transcript.pyannote[101].speaker SPEAKER_00
transcript.pyannote[101].start 404.49096875
transcript.pyannote[101].end 410.02596875
transcript.pyannote[102].speaker SPEAKER_01
transcript.pyannote[102].start 405.16596875
transcript.pyannote[102].end 405.41909375
transcript.pyannote[103].speaker SPEAKER_00
transcript.pyannote[103].start 410.98784375
transcript.pyannote[103].end 418.96971875
transcript.pyannote[104].speaker SPEAKER_01
transcript.pyannote[104].start 418.96971875
transcript.pyannote[104].end 419.44221875
transcript.pyannote[105].speaker SPEAKER_00
transcript.pyannote[105].start 419.44221875
transcript.pyannote[105].end 419.76284375
transcript.pyannote[106].speaker SPEAKER_00
transcript.pyannote[106].start 420.03284375
transcript.pyannote[106].end 421.26471875
transcript.pyannote[107].speaker SPEAKER_00
transcript.pyannote[107].start 422.04096875
transcript.pyannote[107].end 425.66909375
transcript.pyannote[108].speaker SPEAKER_01
transcript.pyannote[108].start 425.66909375
transcript.pyannote[108].end 429.31409375
transcript.pyannote[109].speaker SPEAKER_00
transcript.pyannote[109].start 427.37346875
transcript.pyannote[109].end 428.38596875
transcript.pyannote[110].speaker SPEAKER_00
transcript.pyannote[110].start 429.31409375
transcript.pyannote[110].end 429.36471875
transcript.pyannote[111].speaker SPEAKER_01
transcript.pyannote[111].start 429.73596875
transcript.pyannote[111].end 430.64721875
transcript.pyannote[112].speaker SPEAKER_00
transcript.pyannote[112].start 430.64721875
transcript.pyannote[112].end 430.66409375
transcript.pyannote[113].speaker SPEAKER_01
transcript.pyannote[113].start 431.01846875
transcript.pyannote[113].end 431.03534375
transcript.pyannote[114].speaker SPEAKER_00
transcript.pyannote[114].start 431.03534375
transcript.pyannote[114].end 432.26721875
transcript.pyannote[115].speaker SPEAKER_00
transcript.pyannote[115].start 432.92534375
transcript.pyannote[115].end 437.97096875
transcript.pyannote[116].speaker SPEAKER_01
transcript.pyannote[116].start 433.48221875
transcript.pyannote[116].end 435.47346875
transcript.pyannote[117].speaker SPEAKER_01
transcript.pyannote[117].start 437.97096875
transcript.pyannote[117].end 439.50659375
transcript.pyannote[118].speaker SPEAKER_01
transcript.pyannote[118].start 440.18159375
transcript.pyannote[118].end 450.72846875
transcript.pyannote[119].speaker SPEAKER_00
transcript.pyannote[119].start 440.23221875
transcript.pyannote[119].end 441.19409375
transcript.pyannote[120].speaker SPEAKER_01
transcript.pyannote[120].start 450.76221875
transcript.pyannote[120].end 452.80409375
transcript.pyannote[121].speaker SPEAKER_00
transcript.pyannote[121].start 452.80409375
transcript.pyannote[121].end 455.36909375
transcript.pyannote[122].speaker SPEAKER_01
transcript.pyannote[122].start 455.36909375
transcript.pyannote[122].end 455.68971875
transcript.pyannote[123].speaker SPEAKER_00
transcript.pyannote[123].start 455.68971875
transcript.pyannote[123].end 455.70659375
transcript.pyannote[124].speaker SPEAKER_00
transcript.pyannote[124].start 456.21284375
transcript.pyannote[124].end 456.75284375
transcript.pyannote[125].speaker SPEAKER_01
transcript.pyannote[125].start 456.75284375
transcript.pyannote[125].end 457.09034375
transcript.pyannote[126].speaker SPEAKER_00
transcript.pyannote[126].start 457.09034375
transcript.pyannote[126].end 460.29659375
transcript.pyannote[127].speaker SPEAKER_01
transcript.pyannote[127].start 459.50346875
transcript.pyannote[127].end 459.92534375
transcript.pyannote[128].speaker SPEAKER_01
transcript.pyannote[128].start 460.24596875
transcript.pyannote[128].end 460.27971875
transcript.pyannote[129].speaker SPEAKER_01
transcript.pyannote[129].start 460.29659375
transcript.pyannote[129].end 462.77721875
transcript.pyannote[130].speaker SPEAKER_00
transcript.pyannote[130].start 462.49034375
transcript.pyannote[130].end 471.85596875
transcript.pyannote[131].speaker SPEAKER_01
transcript.pyannote[131].start 465.24096875
transcript.pyannote[131].end 465.51096875
transcript.pyannote[132].speaker SPEAKER_01
transcript.pyannote[132].start 466.06784375
transcript.pyannote[132].end 466.37159375
transcript.pyannote[133].speaker SPEAKER_01
transcript.pyannote[133].start 471.85596875
transcript.pyannote[133].end 478.80846875
transcript.pyannote[134].speaker SPEAKER_01
transcript.pyannote[134].start 480.02346875
transcript.pyannote[134].end 483.46596875
transcript.pyannote[135].speaker SPEAKER_01
transcript.pyannote[135].start 483.80346875
transcript.pyannote[135].end 485.15346875
transcript.pyannote[136].speaker SPEAKER_01
transcript.pyannote[136].start 485.42346875
transcript.pyannote[136].end 490.92471875
transcript.pyannote[137].speaker SPEAKER_01
transcript.pyannote[137].start 491.16096875
transcript.pyannote[137].end 491.51534375
transcript.pyannote[138].speaker SPEAKER_01
transcript.pyannote[138].start 492.25784375
transcript.pyannote[138].end 494.43471875
transcript.pyannote[139].speaker SPEAKER_00
transcript.pyannote[139].start 494.43471875
transcript.pyannote[139].end 497.86034375
transcript.pyannote[140].speaker SPEAKER_00
transcript.pyannote[140].start 498.14721875
transcript.pyannote[140].end 501.52221875
transcript.pyannote[141].speaker SPEAKER_00
transcript.pyannote[141].start 502.06221875
transcript.pyannote[141].end 510.48284375
transcript.pyannote[142].speaker SPEAKER_00
transcript.pyannote[142].start 511.14096875
transcript.pyannote[142].end 513.68909375
transcript.pyannote[143].speaker SPEAKER_00
transcript.pyannote[143].start 515.10659375
transcript.pyannote[143].end 518.39721875
transcript.pyannote[144].speaker SPEAKER_00
transcript.pyannote[144].start 518.76846875
transcript.pyannote[144].end 522.36284375
transcript.pyannote[145].speaker SPEAKER_00
transcript.pyannote[145].start 523.49346875
transcript.pyannote[145].end 528.74159375
transcript.pyannote[146].speaker SPEAKER_00
transcript.pyannote[146].start 529.58534375
transcript.pyannote[146].end 530.90159375
transcript.pyannote[147].speaker SPEAKER_00
transcript.pyannote[147].start 531.13784375
transcript.pyannote[147].end 533.44971875
transcript.pyannote[148].speaker SPEAKER_00
transcript.pyannote[148].start 533.53409375
transcript.pyannote[148].end 540.62159375
transcript.pyannote[149].speaker SPEAKER_00
transcript.pyannote[149].start 541.39784375
transcript.pyannote[149].end 556.46721875
transcript.pyannote[150].speaker SPEAKER_01
transcript.pyannote[150].start 556.46721875
transcript.pyannote[150].end 585.96471875
transcript.pyannote[151].speaker SPEAKER_01
transcript.pyannote[151].start 586.65659375
transcript.pyannote[151].end 587.06159375
transcript.pyannote[152].speaker SPEAKER_01
transcript.pyannote[152].start 587.23034375
transcript.pyannote[152].end 587.34846875
transcript.pyannote[153].speaker SPEAKER_00
transcript.pyannote[153].start 587.34846875
transcript.pyannote[153].end 590.67284375
transcript.pyannote[154].speaker SPEAKER_01
transcript.pyannote[154].start 588.09096875
transcript.pyannote[154].end 588.58034375
transcript.pyannote[155].speaker SPEAKER_00
transcript.pyannote[155].start 591.21284375
transcript.pyannote[155].end 598.40159375
transcript.pyannote[156].speaker SPEAKER_00
transcript.pyannote[156].start 598.84034375
transcript.pyannote[156].end 612.99846875
transcript.pyannote[157].speaker SPEAKER_01
transcript.pyannote[157].start 601.55721875
transcript.pyannote[157].end 603.27846875
transcript.pyannote[158].speaker SPEAKER_00
transcript.pyannote[158].start 613.60596875
transcript.pyannote[158].end 633.87284375
transcript.pyannote[159].speaker SPEAKER_00
transcript.pyannote[159].start 634.10909375
transcript.pyannote[159].end 635.08784375
transcript.pyannote[160].speaker SPEAKER_00
transcript.pyannote[160].start 635.37471875
transcript.pyannote[160].end 639.15471875
transcript.pyannote[161].speaker SPEAKER_01
transcript.pyannote[161].start 639.98159375
transcript.pyannote[161].end 645.12846875
transcript.pyannote[162].speaker SPEAKER_01
transcript.pyannote[162].start 646.15784375
transcript.pyannote[162].end 651.05159375
transcript.pyannote[163].speaker SPEAKER_01
transcript.pyannote[163].start 652.03034375
transcript.pyannote[163].end 652.55346875
transcript.pyannote[164].speaker SPEAKER_01
transcript.pyannote[164].start 654.20721875
transcript.pyannote[164].end 660.50159375
transcript.pyannote[165].speaker SPEAKER_01
transcript.pyannote[165].start 661.41284375
transcript.pyannote[165].end 665.59784375
transcript.pyannote[166].speaker SPEAKER_01
transcript.pyannote[166].start 665.91846875
transcript.pyannote[166].end 666.71159375
transcript.pyannote[167].speaker SPEAKER_01
transcript.pyannote[167].start 667.75784375
transcript.pyannote[167].end 669.25971875
transcript.pyannote[168].speaker SPEAKER_01
transcript.pyannote[168].start 669.79971875
transcript.pyannote[168].end 670.55909375
transcript.pyannote[169].speaker SPEAKER_01
transcript.pyannote[169].start 670.96409375
transcript.pyannote[169].end 672.61784375
transcript.pyannote[170].speaker SPEAKER_01
transcript.pyannote[170].start 673.15784375
transcript.pyannote[170].end 680.32971875
transcript.pyannote[171].speaker SPEAKER_01
transcript.pyannote[171].start 680.90346875
transcript.pyannote[171].end 682.05096875
transcript.pyannote[172].speaker SPEAKER_01
transcript.pyannote[172].start 682.47284375
transcript.pyannote[172].end 688.46346875
transcript.pyannote[173].speaker SPEAKER_01
transcript.pyannote[173].start 689.02034375
transcript.pyannote[173].end 691.95659375
transcript.pyannote[174].speaker SPEAKER_01
transcript.pyannote[174].start 692.27721875
transcript.pyannote[174].end 695.41596875
transcript.pyannote[175].speaker SPEAKER_00
transcript.pyannote[175].start 692.69909375
transcript.pyannote[175].end 692.81721875
transcript.pyannote[176].speaker SPEAKER_01
transcript.pyannote[176].start 696.34409375
transcript.pyannote[176].end 701.67659375
transcript.pyannote[177].speaker SPEAKER_01
transcript.pyannote[177].start 702.23346875
transcript.pyannote[177].end 703.11096875
transcript.pyannote[178].speaker SPEAKER_01
transcript.pyannote[178].start 703.44846875
transcript.pyannote[178].end 709.96221875
transcript.pyannote[179].speaker SPEAKER_01
transcript.pyannote[179].start 711.14346875
transcript.pyannote[179].end 711.75096875
transcript.pyannote[180].speaker SPEAKER_01
transcript.pyannote[180].start 713.50596875
transcript.pyannote[180].end 713.87721875
transcript.pyannote[181].speaker SPEAKER_01
transcript.pyannote[181].start 714.53534375
transcript.pyannote[181].end 717.52221875
transcript.whisperx[0].start 7.074
transcript.whisperx[0].end 9.156
transcript.whisperx[0].text 謝謝主席,祝主席高票當選
transcript.whisperx[1].start 24.113
transcript.whisperx[1].end 31.499
transcript.whisperx[1].text 我問你,現在大家都在銷購票、AI、晶片全部參與就業在高科技有多少從業人口?我看一百萬大概一百萬左右,九十到一百萬然後傳統產業千萬萬?當然比較多
transcript.whisperx[2].start 51.499
transcript.whisperx[2].end 56.071
transcript.whisperx[2].text 那現在這裡的高科技,大家這樣幼小小的,年紀都幾百萬五百、六百、七百、八百
transcript.whisperx[3].start 60.891
transcript.whisperx[3].end 85.74
transcript.whisperx[3].text 現在很多我們的醫生都不敢做,我們兩個同學都七八八半,要在這裡...我跟妳報告喔,志願醫師他要值班,然後在高科技也要值班,都一樣,但是他值班,他有五百、四百的年薪,他值班一個月十來萬,尤其PGY的都是十萬上下,在台大醫院的PGY九萬塊而已,
transcript.whisperx[4].start 88.253
transcript.whisperx[4].end 102.326
transcript.whisperx[4].text 在彩通華人醫院可以拿到十二、十三萬差很多有關於心理不平衡現在年輕人不像你想的那樣所以這個是很麻煩的事那整個產業失衡那這邊一千多萬都低薪所以你今天在說平等化、性別,都是低薪造成我也有提兩個案
transcript.whisperx[5].start 115.689
transcript.whisperx[5].end 118.33
transcript.whisperx[5].text 要雙清都請滿才能延長三個月請滿六個月
transcript.whisperx[6].start 130.629
transcript.whisperx[6].end 154.81
transcript.whisperx[6].text 停滿六個月 要請滿 要請滿六個月所以爸爸媽媽都要六個月 六個月 對啊你如果這個可以簽三個月 才可以多三個月啊這三個月你是多多少 用他投保薪資多多少一樣喔 一樣是八成 八成啊你現在投保薪資現在連的上限到四萬五千多嘛所以我們要把它打開 你就把它調去十萬就好啦
transcript.whisperx[7].start 159.092
transcript.whisperx[7].end 172.337
transcript.whisperx[7].text 市長你有反對嗎第一個這個投保薪資的上限他會牽涉到因為他除了睡暈留庭給付以外他其實也會是失業給付
transcript.whisperx[8].start 174.504
transcript.whisperx[8].end 200.62
transcript.whisperx[8].text 他也是會是失業給付的計算的基礎所以我們的確也會需要考慮比方說假如像委員說的達到10萬那六成的失業給付算起來的話他一個月可以拿6萬那他拿6萬是比很多在工作的人還多也就是失業的人可能會拿的比工作的人還多因為現在很多人他的薪資是高於這個4萬多塊嘛
transcript.whisperx[9].start 201.721
transcript.whisperx[9].end 221.251
transcript.whisperx[9].text 所以投保薪資都高薪低報嘛所以委員我們是覺得4萬多的確這個上限應該要打開可是上限開到哪裡其實會有好幾個因素需要考慮這個我知道啦你現在遷移法動全生投保的部隊那麼龐大怕你打不掉嘛我告訴你現在醫生組織醫師
transcript.whisperx[10].start 224.144
transcript.whisperx[10].end 236.926
transcript.whisperx[10].text 他一個月 投保 健保 你知道健保被人欺負多少呢 健保是用以前是18萬 現在搞不好更多 市長市長 現在是用多少投保
transcript.whisperx[11].start 239.478
transcript.whisperx[11].end 248.03
transcript.whisperx[11].text 就是說醫生去投保用健保給他求情,5.25%那是很恐怖的,現在你這邊他每個月都要錄檢討,錄到他要退休,都死了
transcript.whisperx[12].start 257.236
transcript.whisperx[12].end 271.542
transcript.whisperx[12].text 那四萬幾塊 八百塊 都只剩兩萬幾塊 這差距太大在美國 美國醫師 他如果繳的社保很高他退休之後 他一個月可以領18萬台幣
transcript.whisperx[13].start 273.923
transcript.whisperx[13].end 283.251
transcript.whisperx[13].text 18萬台幣,也不算多啦,18萬台幣所以如果回到台灣,繼續這個職業,他們如果超過18萬,美國會把他踢走如果要查就要把他踢走所以現在回到台灣,都希望可以兼職,兼職,不要要顧著做這都是技巧啦我現在要講的就是,你現在這個薪資4萬6千多
transcript.whisperx[14].start 299.825
transcript.whisperx[14].end 327.197
transcript.whisperx[14].text 四五八零零四五八零零你現在想應該有我們應該可以到五萬五萬不是十萬啦齁目標啦 目標十萬啦齁五萬啦 五萬啊所以你要八成還是六成八成因為流停是八成就是說你講津貼喔 津貼是津貼因為流停今天是八成啊現在又過來就是那個
transcript.whisperx[15].start 332.864
transcript.whisperx[15].end 354.67
transcript.whisperx[15].text 產價是全額8週變12週那4個月那4週是自由決定還是強制各位說明其實是強制12週但是這個孕產媽媽她可以自己決定要不要提前消假她自己決定她可以決定然後雇主不得拒絕
transcript.whisperx[16].start 356.639
transcript.whisperx[16].end 383.632
transcript.whisperx[16].text 當然啊 趕快回來職場比較好啊有些 因為各個孕產的媽媽她很多身體的狀況會不一樣有一些她需要比較長的恢復期有一些可能覺得她其實身體恢復差不多如果在有專業醫生的確認之下她可以提前回到職場有些她會說她不想要在家裡綁這麼久也有這樣的聲音所以我們這次在設計上面有一個給她自己選擇的選擇選擇權但是雇主 她選擇了 雇主是不可以拒絕的好
transcript.whisperx[17].start 386.661
transcript.whisperx[17].end 409.838
transcript.whisperx[17].text 所以你現在是兩筆錢都有付就對了?產價是產價,運營流停是運營流停,運營流停是八成,他的投保薪是八成產價是他那四周他的勞工的薪資,前面的八周過去是企業付目前就是企業付,目前八周的產價的薪資就是企業付
transcript.whisperx[18].start 411.055
transcript.whisperx[18].end 435.373
transcript.whisperx[18].text 那現在是多的這四週他的薪資 請的這個勞工的薪資是由政府來付就是投保薪資的主權不是 他原本的薪資他不是投保薪資產價並不是放在社會保險裡面的他原來的薪資啊對 他原本的薪資你還要10萬你有10萬嗎我們現在就是要就是我們這個薪資的部分我們全國的薪資100%就是要付啊這樣就有差距出來了
transcript.whisperx[19].start 440.257
transcript.whisperx[19].end 454.473
transcript.whisperx[19].text 所以前8週是僱主要付錢你們不付嘛那後面多出來的4週是你們付他請多少你就付多少僱主付完以後可以來跟我們請您補貼
transcript.whisperx[20].start 456.275
transcript.whisperx[20].end 477.382
transcript.whisperx[20].text 插額就對了不是插額就是內四周的四周缺額的補貼雇主來申請你不給雇主還是對就是雇主他先把這個薪資給付給這個員工然後他可以來跟我們申請說這個四周的薪資那由政府來補貼好那第二個剛剛講的津貼今天你說三個月那我的提案是提12個月
transcript.whisperx[21].start 481.07
transcript.whisperx[21].end 501.141
transcript.whisperx[21].text 你說要拿去三個月,你就要他們爸爸媽媽都請六個月過了,待會可以去請一天,這樣嗎?對你不說這樣很麻煩跟文說明,這裡面有個非常非常重要的考量,今天如果我不設任何前提,就把
transcript.whisperx[22].start 502.402
transcript.whisperx[22].end 528.532
transcript.whisperx[22].text 暈流停的津貼期間給延長的時候很有可能造成的狀況是會大部分都是由女性來請這個照顧責任又集中在女性身上所以我們這一次在制度的設計很重要的一個一個用意就是要鼓勵男性來請這是為什麼我們要兩個爸爸媽媽都請滿六個月才有多的
transcript.whisperx[23].start 529.645
transcript.whisperx[23].end 547.84
transcript.whisperx[23].text 三个月的原因就是为了这个制度而现在在各个国家在设计育儿友善的职场制度里面都非常强调要更大程度的设计让爸爸来请的幼婴而不是直接我就把6个月变12个月18个月如果这样的话你可以想象
transcript.whisperx[24].start 548.26
transcript.whisperx[24].end 569.879
transcript.whisperx[24].text 大部分很有可能在職場上面就都是女性必須承擔這個照顧的任務這可能會讓性別不平等的狀況加劇這是我們設計的誘因那最後再回到我剛剛第一個就是說這個產業失衡那這些高所得人都拉高我們的GDP拉高我們的個人平均所得但是這些傳統產業是沒有享受到
transcript.whisperx[25].start 571.5
transcript.whisperx[25].end 584.404
transcript.whisperx[25].text 你做勞工最高領導人,你有什麼想法要彌補一下因為就差太多了,一天的證交稅可以收到五十幾億,嚇死人
transcript.whisperx[26].start 591.687
transcript.whisperx[26].end 611.977
transcript.whisperx[26].text 其實現在整個執政的團隊也是非常非常大的成高的程度在考慮我們怎麼用科技業的紅利所得到的國家的稅收來去協助這個更多廣大的基層勞工所以這一次為什麼我們會提出我們現在很多的方案其實我們認為
transcript.whisperx[27].start 613.658
transcript.whisperx[27].end 638.906
transcript.whisperx[27].text 很多企業如果他負擔的部分我們政府可以幫忙出尤其是對這個中小企業我們可以幫忙出其實某個部分我們就是把科技業他繳稅帶來的紅利希望能夠變成是全民的富利或者是這科技的紅利變成全民或勞工的富利啦所以我們用這個方式來希望來補貼更多育兒友善可能會多出來的支出也讓企業更願意配合
transcript.whisperx[28].start 640.069
transcript.whisperx[28].end 669.095
transcript.whisperx[28].text 我那天看到美國Amazon就是亞馬遜的老闆就是光頭那個他說貝佐斯嘛齊一個老婆很漂亮那個他說美國的護士年薪是七萬五千塊美金他說年薪七萬五千塊以下的人不用講帥我覺得他很棒啊
transcript.whisperx[29].start 669.844
transcript.whisperx[29].end 692.588
transcript.whisperx[29].text 我跟他 半夜爬起來跟他拍手齁希望我 表示說美國的護士一年是捏七百五美金一年兩百幾萬所以我們台灣的 大家的薪水都太低了嘛他說七百五千塊年薪以下的人不用繳稅那這樣的話我們在座的很多人都不用繳稅啊你說這是貝佐士的主張嗎對
transcript.whisperx[30].start 696.397
transcript.whisperx[30].end 709.581
transcript.whisperx[30].text 他說這些營利事業所得稅跟個人所得稅要大幅的增加這樣才是公平公正啦我覺得這樣是對的所以普通人往這個方向來努力啦