Interdisciplinary Studies and the Bridging Disciplines: A Matter of Process
Bibliographic record
Abstract
Bridging disciplines have much to teach regarding how to combine analytical tools to tackle problems and questions that cross traditional disciplinary boundaries. This article explores interdisciplinary aspects of two long established bridging disciplines--geography and anthropology--in order to consider what the relatively young undertaking labeled “interdisciplinary studies” can learn from their long existence. It considers the fallacy of nomothetic claim as well as the fruitful production of solutions by viewing process (methodology), not domain (academic turf), as the key to interdisciplinary success. Staking claim to interdisciplinarity is shown to be unproductive while finding the need for interdisciplinary approaches and following the mandates of that need strengthens both the disciplines and interdisciplinary studies.
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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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.114 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.009 |
| 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; 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".