Bibliographic record
Abstract
Reflections on crossing disciplinary lines abound in the scientific community. Can cross-disciplinary approaches, with all their complexity and particularities, provide the way forward in the search for practical solutions to real-world problems? In this article, the author addresses how the debate on cross-disciplinarization pertains to the field of policy evaluation. Evaluation is appropriate terrain for such a discussion as this particular field of social science seeks to produce useful knowledge for both managers and policy makers. As such, the author offers a general account of the advantages and disadvantages of cross-disciplinary evaluation. Because evaluation requires close collaboration between individuals from different domains and backgrounds, the author further outlines the specific challenges that face the practitioner when conducting a cross-disciplinary evaluation.
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 | 0.327 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.014 | 0.133 |
| Scholarly communication | 0.047 | 0.075 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".