The IJHPR’s growing scientific impact
Bibliographic record
Abstract
The Israel Journal of Health Policy Research (IJHPR) was launched in 2012, with a mission that included fostering intensive intellectual interactions among health policy scholars in Israel and abroad. Now, as the journal approaches the end of its seventh year of publication, we can all be proud that this component of our mission is increasingly being realized.As of the end of November 2018, the Web of Science included 404 articles published by the IJHPR. These IJHPR articles had generated 1023 citations via 847 citing articles. Just over 70% of those citing articles were in journals other than the IJHPR, with the vast majority of those being in non-Israeli journals. The authors of the citing articles were most often based in institutions in the US (35%), Israel (33%), England (9%) or Canada (7%).Looking to the future, we hope that the IJHPR will receive even more submissions from authors based in Israel or other countries that are well-designed data-based studies; thoughtful, comprehensive policy analyses; or important integrations of a body of knowledge. In all instances, these should be relevant to Israeli health policy and health care. We hope that many, ideally most, will also be relevant to scholars, policymakers and professionals in other countries.
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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 | 0.022 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".