Bibliographic record
Abstract
The fetch results page now shows progress updates for unfinished jobs, as well as live-updates. This also includes support for terminating all pending jobs, as well as terminating jobs while they're running. The plot-dates task has been split into two analysis task types, one of which counts the number of articles in each year in the dataset, and one of which counts the number of occurrences of a particular term in the articles within a dataset, and graphs those occurrences by year. There is now a cooccurrence analyzer that will return significant cooccurrences that occur at some distance from one another, as opposed to collocations, which are immediate neighbors. Add a completely redesigned advanced search page, including autocomplete for authors and journals. Add an optimized WordFrequencyAnalyzer that can be called when only a single block is requested. The date-plotting jobs now include intervening years with a "zero" value in the downloaded CSV and graph, instead of leaving them out of the analysis entirely. Several jobs now support returning "all words" or "all pairs" when practicable. Fix all sorts of bugs and security vulnerabilities UI refresh/rewrite to increase stability and reduce development time.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.440 | 0.492 |
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".