Ecosystem Management Research: Clarifying the Concept of Interdisciplinary Work
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it