Bibliographic record
Abstract
de Vries's travel, Gary Libecap's Sabbatical, and changes in editorial office personnel at Berkeley.Jan is now ably assisted by Heath Pearson and Edna Tow.Gary gratefully acknowledges the contribution of the University of Arizona Economics Department toward editorial office expenses over the last four years.The editors are also grateful for Tom Weiss's expert management of the JOURNAL' S finances.At the end of June 2000, Gary's term as co-editor for the North American office will end and Gavin Wright will begin his term as co-editor.1998/99 saw a decline in new submissions relative to last year from 112 to 90.As Figure 1 shows, the trend in the number of submissions for the last 20 years appears stable, though 1998/99 equaled 1993/94 as the lowest year.The editors are satisfied that the quality of submissions has remained high.Tables 1 through 3 show the distribution of new submissions by topic, region, and era.Each of these measures of the scope of the JOURNAL'S coverage reflects a continuation of the breadth that is a central goal of this publication.The decline in the number of new submissions was more than offset by a rise in the number of resubmissions and meant that the offices handled ten more papers than last year.Given that resubmissions formed a larger percentage of papers, the fact that the acceptance rate was higher and both the rejection and revise-and-resubmit rates were lower than last year's is not surprising (see Table 4).The time required for a decision lengthened slightly.
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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 | 0.014 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.136 | 0.139 |
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".