The Hidden Paths of Category Research: Climbing new heights and slippery slopes
Bibliographic record
Abstract
Category research has flourished over the last decade. While this body of work has prioritized the behavioral and economic consequences of stable classification systems, the papers in this special issue challenge this orientation by highlighting the importance of category dynamics for improving our understanding of markets and fields. We show how these papers support the emergence of category maintenance, the recategorization of mature categories, and the consolidation of new categories as understudied phenomena and as the next research challenges to pursue. After connecting the main findings of the papers in this special issue into a unified process model, we discuss various alternative pathways to further explore those challenges. We also point to how this theoretical endeavor runs on slippery slopes and might lead to cul-de-sacs such as terminological balkanization. We conclude by highlighting the need for developing a more comprehensive understanding of category dynamics.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.021 | 0.056 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".