1study,estimate_as_printed,uncertainty_as_printed,sample_as_printed,definition,locator,harmonised_estimate,harmonised_se,harmonisable,poolable,transformation,reason_if_not2S1,"ExpCon coefficient (Model 2, LogToT) = -0.24 (marked '.', p<.1); text: effect of AI = 21% faster","SE (0.14); 95% CI = [-0.51, 0.03]; p=0.086","n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI","OLS coefficient on experimental condition (AI vs no-AI exposure) with natural-log time on task as dependent variable, controlling for developer-level factors (AvgProgHrsDay, Level); one complex enterprise coding task, randomised","arxiv-2410.12944v3, Table III, row 'ExpCon', column (2) 'Dev' (value -0.24., SE line (0.14)); CI and p-value in Section VI-C text",-0.24,0.14,True,True,theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Cross-check from printed CI: (0.03-(-0.51))/(2*1.96) = 0.1378,3S2,"+19% (""allowing AI actually increases completion time by 19%""; ""implement issues in 19% more time on average"")","none printed in extract (95% CI shown only in Figure 1, not reproduced)","16 developers, 246 tasks (136 issues AI-allowed, 110 AI-disallowed)","S = E[completion time with AI allowed]/E[completion time with AI disallowed] - 1, estimated as exp(beta)-1 from log-linear OLS of total implementation time on AI indicator and log forecast time; issue-level randomisation","arxiv-2507.09089v2, Abstract and Section 3 'Results' opening paragraph (19%); estimator in Section 2.3 and Appendix D.1-D.3",0.17395330712343798,,True,False,"theta = ln(1 + c) = ln(1 + 0.19) = 0.1740 (equivalently theta = beta-hat, since S = exp(beta)-1)","No SE, CI or p-value for the 19% estimate is printed in the extract; its confidence interval appears only in Figure 1, which is not reproduced. SE cannot be obtained, so not poolable."4S3,"-29.79%*** (Treatment 44.29 min, Control 63.10 min; Overall 53.84 min)",significance stars only: *** (0<p<=0.01),181 subjects completed the task satisfactorily (treatment/control split not printed in extract),"Percentage difference in mean task duration (minutes), Copilot vs control, holding accuracy constant (control duration predicted at treatment accuracy via a control-group regression of duration on accuracy); all content, randomised","arxiv-2411.01067v2, Table 3, block 'Aggregate Results', row 'All Content', columns Treatment / Control / Improvement",-0.353679434958934,,True,False,theta = ln(1 + c) = ln(1 - 0.2979) = -0.3537 (ratio of printed means: ln(44.29/63.10) = -0.3540),"Only significance stars (***, p<=0.01) are printed; no SE, CI or exact p-value, so SE cannot be obtained and the study is not poolable."5S4,"% Time Savings 11.1 (Aware 50.5 s per email, Control 56.9 s per email), Full Corpus",p-value 0.471,"Aware N=56, Control N=52 (167 analysts across three arms; 25-email queues)","Percentage time saving in mean time per email (Aware arm: agent verdicts/rationale and agent-ordered queue vs Control: random order, no AI), across the full queue; randomised","arxiv-2511.13860v1, Section 3.3, Table 3, column 'Full Corpus', rows 'Aware Time per Email (s)', 'Control Time per Email (s)', '% Time Savings', 'p-value'",-0.11765804346823246,0.1632205919720678,True,True,c = -0.111 (11.1% saving); theta = ln(1 - 0.111) = -0.1177; approximate SE = |theta|/z_(1-0.471/2) = 0.1177/0.7209 = 0.1632 (APPROXIMATE SE recovered from p-value). Ratio of printed means: ln(50.5/56.9) = -0.1193,6S5,"Treat x Post = -0.278 (Output, column 3, panel A, users with affiliation)",SE (0.295); no stars,N = 18332,"Difference-in-differences coefficient on the aggregate GitHub Output measure (sum of Push, Pull Requests, PR comments, commit comments, Create, Issues events; the text describes log counts for these measures) for Italian vs Austrian/French users, first two business days after vs before the ChatGPT ban","arxiv-2304.09339v1, Table 1, panel A (Pre 27/28.03 - Post 03/04.04), row 'Treat x Post', column (3) 'Output'; N from row 'N'",,,False,False,none applied (not harmonisable),"The printed effect is on output counts (GitHub events), not task-completion time on the same tasks, and it comes from a natural experiment (withdrawal of access via a national ban, DiD) rather than a randomised AI-vs-no-AI comparison of completion times. It cannot be mapped onto theta = ln(T_AI/T_noAI)."7