International Law Systematic Review Dataset v1.0
Bibliographic record
Abstract
International treaties are often used by countries to address concerns that cross national boundaries, including the environment, human rights, humanitarian crises, maritime issues, security and trade. While over 250,000 international treaties exist, no study has ever compiled and analyzed decades of research assessing their effectiveness. Ten electronic bibliographic databases were searched from inception to December 2017: Applied Social Sciences Index & Abstracts, CINAHL, Global Health, International Bibliography of Social Sciences, International Political Science Abstracts, MEDLINE, PAIS International, Social Sciences Abstracts, Social Sciences Citation Index, and Worldwide Political Science Abstracts. The study selection included publicly available peer-reviewed studies and gray literature (e.g., dissertations) that aimed to quantitatively measure an international treaty’s effect on any objective and quantifiable outcome. Further details on the review of titles and abstracts for eligibility, final consensus for inclusion, aggregation and deduplication using EndNote, and assessment for bias using Cochrane’s ROBINS-I tool are found in SI Appendix. This dataset contains information on treaty characteristics from 199 unique quantitative estimates evaluating 53 unique treaties. At the time of study inception, there was no existing systematic field-wide evidence synthesis that could provide the information upon which to evaluate the effects of international treaties. Further details are available in the accompanying article: Hoffman SJ, Baral P, Rogers van Katwyk S, Sritharan L, Hughsam M, Randhawa H, Lin G, Campbell S, Campus B, Dantas M, Foroughian N, Groux G, Gunn E, Guyatt G, Habibi R, Karabit M, Karir A, Kruja K, Lavis JN, Lee O, Li B, Nagi R, Naicker K, Røttingen J, Sahar N, Srivastava A, Tejpar A, Tran M, Zhang Y, Zhou Q, Poirier MJP. International treaties have mostly failed to produce their intended effects. PNAS. 2022 Aug 2; 119;32 https://doi.org/10.1073/pnas.2122854119 Statement of Contributions: Designed research: S.J.H. Performed research: S.J.H., P.B., S.R.V.K., L.S., M.H., H.R., G.L., S.C., B.C., M.D., N.F., G. Groux, E.G., G. Guyatt, R.H., M.K., A.K., K.K., J.N.L., O.L., B.L., K.N., R.N., J.-A.R., A.T., M.T., N.S., A.S., Y.-q.Z., Q.Z., M.J.P.P. Contributed new reagents/analytic tools: S.J.H., S.R.V.K., G.L., G.G., Q.Z., M.J.P.P. Analyzed data: S.J.H., G.L., Q.Z., M.J.P.P. Wrote the paper: S.J.H., P.B., S.R.V.K., L.S., M.H., H.R., G.L., S.C., B.C., M.D., N.F., G. Groux, E.G., G. Guyatt, R.H., M.K., A.K., K.K., J.N.L., O.L., B.L., K.N., R.N., J.-A.R., A.T., M.T., N.S., A.S., Y.-q.Z., Q.Z., M.J.P.P. We thank A. Agarwal, N. Jedrzejko, V. Lui, N. Natt, J. Petropoulos, and J. Syrotuik for their help with developing the search strategy; P. Alexander for contributing to risk of bias assessment; as well as M. Chen (Chinese), A. Yu (Chinese), W. Boer (Dutch), T. Huijits (Dutch), J. Bailey (German), S. Schandelmaie (German), C. Barbui (Italian), A. Iorio (Italian), A. Skordai (Hungarian), and J. J. Yepes-Nu~nez (Spanish) for their assistance with text translations. Funding was provided by Research Council of Norway’s Global Health & Vaccination Programme GLOBVAC Project 234608 (to S.J.H.); Canadian Institutes of Health Research 172982 (to S.J.H.); and Ontario Government’s Ministry of Research, Innovation & Science ER16-12-197 (to S.J.H.).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.205 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.026 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.294 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".