Discriminating between empirical studies and nonempirical works using automated text classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews. METHODS: The algorithms were trained and validated with 8050 database records, which had previously been manually categorized as empirical or nonempirical. A Boolean mixed filter developed for filtering MEDLINE records (title, abstract, keywords, and full texts) was used as a baseline. The set of features (eg, characteristics from the data) included observable terms and concepts extracted from a metathesaurus. The efficiency of the approaches was measured using sensitivity, precision, specificity, and accuracy. RESULTS: The decision trees algorithm demonstrated the highest performance, surpassing the accuracy of the Boolean mixed filter by 30%. The use of full texts did not result in significant gains compared with title, abstract, keywords, and records. Results also showed that mixing concepts with observable terms can improve the classification. SIGNIFICANCE: Screening of records, identified in bibliographic databases, for relevant studies to include in systematic reviews can be accelerated with automated text classification.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.440 | 0.474 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it