Beyond setting conservation targets: Q-method as a powerful tool to collectively set an action plan agenda
Bibliographic record
Abstract
Nature conservation begins with detailed knowledge of the ecosystem based on inventories and maps. A difficult part of the conservation process subsequently starts, namely, the design of an action plan that achieves the desired protection outcome. As both funding and time are limited, conservation is subject to difficult trade-offs among competing land uses. We present a novel approach based on the Q-method to support local stakeholders that go beyond its usual use in assisting decision-making. We suggest a new usage of the Q-method: a tool to support conservation action prioritization. Our results indicate that the Q-method has valuable attributes, as (1) it encourages individual reflection on one’s own priorities; (2) it identifies different prioritization patterns among respondents; (3) it provides input to later collective discussions, ultimately contributing to establishing consensus; (4) it brings additional arguments to conservation planners based on the latter’s declared priorities. Overall, this use of Q-method can help stakeholders prioritize conservation actions, a crucial step toward achieving ecologically and socially robust conservation action plan.
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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 | 0.116 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".