Necessary but challenging: Multiple disciplinary approaches to solving conservation problems
Bibliographic record
Abstract
Contemporary conservation problems are typically positioned at the interface of complex ecological and human systems. Traditional approaches aiming to compartmentalize a phenomenon within the confines of a single discipline and failing to engage non-science partners are outmoded and cannot identify solutions that have traction in the social, economic, and political arenas in which conservation actions must operate. As a result, conservation science teams must adopt multiple disciplinary approaches that bridge not only academic disciplines but also the political and social realms and engage relevant partners. Five reasons are presented that outline why conservation problems demand multiple disciplinary approaches in order to move forward because: (i) socio-ecological systems are complex, (ii) multiple perspectives are better than one, (iii) the results of research must influence practice, (iv) the heterogeneity of scale necessitates it, and (v) conservation involves compromise. Presenting reasons that support multiple disciplinarity demands a review of the barriers that impede this process, as we are far from attaining a model or framework that is applicable in all contexts. Two challenges that impede multiple disciplinarity are discussed, in addition to pragmatic solutions that conservation scientists and practitioners can adopt in their work. Overall, conservation researchers and practitioners are encouraged to explore the multiple disciplinary dimensions of their respective realms to more effectively solve problems in biodiversity and 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 | 0.103 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.025 | 0.112 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 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".