Mess among Disciplines: Interdisciplinarity in Environmental Research
Bibliographic record
Abstract
This paper discusses interdisciplinary collaboration between social and natural scientists from the perspective of ‘mess’. The literature on interdisciplinarity has generated a series of conventions about what it means to conduct interdisciplinary research. Building on the experience of a research project that brought social and natural scientists together with local residents to study flooding, we argue that interdisciplinarity can be understood as a response to mess, to the irreducibly complex problems of the world. Mess can be dealt with as an epistemological or ontological problem. We argue that discursive conventions focus on the epistemological dimensions of mess and thus have their limits. By considering the ontological dimensions of mess the whole range of objects that are involved in interdisciplinary research is brought into focus.
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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 | 0.087 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.016 | 0.099 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".