Gap analysis of social science resources for conservation practice
Bibliographic record
Abstract
Conservation is an inherently social process-people collectively endeavor to enact conservation. Yet, in conservation social science, research methodologies, training, and competency are less common than in natural sciences. Globally, formal education and training in the social sciences are often unavailable or inaccessible to conservation practitioners, and nonformal education may help fill this gap. To identify potential opportunities, we implemented a global survey of practitioners to identify their knowledge gaps and social science training needs and conducted a gap analysis of available social science training resources. We compiled 449 resources, including 266 English-language and 183 non-English-languages resources into an open-access online database hosted by the Conservation Social Science Partnership. Resources were categorized as communication, data collection, ethics and human rights, intervention, impact evaluation, or analysis. Most resources were open access (90%) and half were specific to conservation practice. Survey responses (n = 90) revealed demand for help with data analyses, research ethics, and human rights considerations. We found a need for organization leaders to prioritize social sciences in conservation, greater diversity of accessible training resources in alternate mediums and languages, resources tailored to conservation contexts, and additional ethics and human rights and data analysis resources.
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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 | 0.049 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".