BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:ReproducibiliTea - Stanford 
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50525881202896
DTSTART:20250910T190000Z
DTEND:20250910T200000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50658127980460
DTSTART:20251008T190000Z
DTEND:20251008T200000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50658127983533
DTSTART:20251112T200000Z
DTEND:20251112T210000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50658127985582
DTSTART:20251210T200000Z
DTEND:20251210T210000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50658127987631
DTSTART:20260114T200000Z
DTEND:20260114T210000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50658127989680
DTSTART:20260211T200000Z
DTEND:20260211T210000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_50658127992753
DTSTART:20260311T190000Z
DTEND:20260311T200000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_52428129265032
DTSTART:20260408T190000Z
DTEND:20260408T200000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_52428129267081
DTSTART:20260513T190000Z
DTEND:20260513T200000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260916T183027Z
UID:tag:localist.com\,2008:EventInstance_53114470290087
DTSTART:20260610T190000Z
DTEND:20260610T200000Z
DESCRIPTION:ReproducibiliTea is an international community of journal clubs
  dedicated to advancing Open Science and improving academic research cultu
 re. \n\nReproducibiliTea at Stanford was launched at 26 October 2022 and w
 elcomes new members. Information on upcoming meetings is presented below\,
  and you can find us on our slack channel\, and join our mailing list repr
 oducibilitea@lists.stanford.edu. The meetings are held every second Wednsd
 ay of the month\, at 12:00.\n\nTo help us prepare for the meetings\, and o
 rder lunch for everyone (free lunches provided) please register for the ne
 xt meeting using the registration form. \n\nThe next meeting is June 10th\
 , 12:00  to 1 pm in LK306 Seminar Classroom (see map plan of the building)
 .\n\nTopic: Scaling Reproducibility: An AI-Assisted Workflow for Large-Sca
 le Replication and Reanalysis\n\nSpeaker: Yiqing Xu\n\nAbstract: Computati
 onal reproducibility is central to scientific credibility\, yet verifying 
 published results at scale remains costly. We develop an AI-assisted workf
 low for automated full-paper replication -- retrieving materials\, reconst
 ructing environments\, executing code\, and matching outputs to point esti
 mates reported in regression tables. We define a universe of all empirical
  and quantitative papers from the three top political science journals (20
 10--2025) and measure stated data availability using automated extraction.
  For a stratified sample of 384 studies\, we apply the workflow to conduct
  full-paper replication\, totaling 3\,523 empirical models. We find that j
 ournal verification requirements\, combined with data archiving mandates\,
  drive reproducibility: the share of fully or largely reproducible papers 
 rises from 20.8% before DA-RT adoption to 82.5% after\, and conditional on
  accessible replication packages\, 92.1% of papers are fully or largely re
 producible (234/254). As a secondary application\, we apply standardized I
 V diagnostics to 84 studies (597 IV specifications among 1\,910 replicated
  models)\, illustrating how automated execution enables systematic reanaly
 sis across heterogeneous empirical settings.\n\nSee the preprint at: https
 ://arxiv.org/abs/2602.16733
GEO:37.43181;-122.175758
LOCATION:Li Ka Shing Center\, LK306
SUMMARY:ReproducibiliTea - Stanford 
URL;VALUE=URI:https://events.stanford.edu/event/copy-of-reproducibilitea-st
 anford-2108
CATEGORIES:Meeting
END:VEVENT
END:VCALENDAR