Bibliographic record
Abstract
What's Changed Fix typo by @havardox in https://github.com/J535D165/recordlinkage/pull/184 Fix usage examples by @martinhohoff in https://github.com/J535D165/recordlinkage/pull/190 Fix links by @andyjessen in https://github.com/J535D165/recordlinkage/pull/186 add threshold None and label docstrings for String by @davidggphy in https://github.com/J535D165/recordlinkage/pull/189 Add support for pandas==2 by @J535D165 in https://github.com/J535D165/recordlinkage/pull/192 Replace setup.py by pyproject.toml by @J535D165 in https://github.com/J535D165/recordlinkage/pull/195 Lint with Ruff and format with Black by @J535D165 in https://github.com/J535D165/recordlinkage/pull/196 Update CI docs generation and CI pipeline by @J535D165 in https://github.com/J535D165/recordlinkage/pull/197 Update the docs CI pipeline by @J535D165 in https://github.com/J535D165/recordlinkage/pull/198 Add pre-commit hooks by @J535D165 in https://github.com/J535D165/recordlinkage/pull/199 New Contributors @havardox made their first contribution in https://github.com/J535D165/recordlinkage/pull/184 @martinhohoff made their first contribution in https://github.com/J535D165/recordlinkage/pull/190 @andyjessen made their first contribution in https://github.com/J535D165/recordlinkage/pull/186 @davidggphy made their first contribution in https://github.com/J535D165/recordlinkage/pull/189 Full Changelog: https://github.com/J535D165/recordlinkage/compare/v0.15...v0.16
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.783 | 0.852 |
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".