Ecosystem Management Research: Clarifying the Concept of Interdisciplinary Work
Bibliographic record
Abstract
Ecosystem management (EM) is a process for addressing environmental problems. It draws on research from multiple disciplines in order to ensure long-term maintenance of socio-ecological systems. The present study evaluates the definition of interdisciplinary work among researchers involved in generating data use (EM). The goal is twofold: to generate further discussions in research supporting EM, and to better situate this research in the broader context of interdisciplinary science. Using an online questionnaire, data was collected from 119 researchers. A cluster analysis identified both distinct and shared understandings of the concept. A logistic regression analysis identified the extent to which personal characteristics and researchers’ understandings of interdisciplinary theory determine definitions of interdisciplinary work. Researchers differ on the terminology but share an understanding about what it is: both a ‘way to do research’ and a ‘way of thinking about research’. Differences between researchers suggest a growing interest in developing deeper engagements with theoretical discussions of interdisciplinarity. Results are discussed in the context of the current state of development of research for EM and its contributions to sustainability.
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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 | 0.143 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.011 | 0.099 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".