Into a new decade
Bibliographic record
Abstract
The 2010s have been exceptionally good for Behavior Research Methods (BRM). The number of papers and submissions almost doubled from 2010 to 2019, and the number of article downloads grew exponentially. In the first 6 months of 2020, there were 800,000 downloads of articles, an amazing number that was unimaginable at the start of the decade. The journal's success is partly due to the good stewardship of the previous editors, who leave big shoes to fill, and partly to the fact that all 5989 papers published since the start in 1968 up to the end of 2019 are freely available for download at the BRM website. Indeed, the Psychonomic Society takes pride in that all articles become open access 1 year after publication, and even before articles become open access, authors can share their articles in view-only form via the 'share this article' link at the BRM website, or make post-prints available through institutional repositories. The Society values open access to research much more than the money it could make by keeping findings behind a paywall. For authors, this benefit is appealing, because their findings become freely available without payment of article processing charges.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.046 | 0.019 |
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".