Bibliographic record
Abstract
Abstract Interdisciplinary research on people, plants, and environmental change (IRPPE) typically requires collaboration among experts who each bring distinct knowledge and skills to bear on the questions at hand. The benefits and challenges of interdisciplinary research in principle are thus confounded by the dynamics of multidisciplinary collaboration in practice. However, broadly trained researchers can do IRPPE with little or no need of collaborators. For them, collaborative challenges may be negligible, but others arise. This paper reflects on experiences doing (mostly) solo research on peoples’ use of trees and their impacts on forests in the Caribbean and Philippines. Multidisciplinary collaborations are often plagued with problems of communication, theoretical disagreement, and methodological incompatibility because the habits and conceits of a rigorous disciplinary education are difficult to undo. These are problems that novel concepts, theory, and analytical frameworks promise but often fail to resolve. By contrast, going solo fosters an epistemic humility and pragmatic sensibility that encourages focused, efficient application of methods, and integration of research findings. Epistemic breadth encourages solo IRPPE researchers to apply theory sparingly and deploy clear concepts and precise analyses of the kind readily grasped by natural and social scientists and policy makers, alike.
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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 | 0.055 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".