Changing trends and persisting biases in three decades of conservation science
Bibliographic record
Abstract
Conservation science is a rapidly developing discipline, and the knowledge base it generates is relevant for practical applications. It is therefore crucial to monitor biases and trends in conservation literature, to track the progress of the discipline and re-align efforts where needed. We evaluated past and present trends in the focus of the conservation literature, and how they relate to conservation needs. We defined the focus of the past literature from 13 published reviews referring to 18,369 article classifications, and the focus of the current literature by analysing 2553 articles published between 2011–2015. We found that some of the historically under-studied biodiversity elements are receiving significantly more attention today, despite being still under-represented. The total proportion of articles on invertebrates, genetic diversity, or aquatic systems is 50%–60% higher today than it was before 2010. However, a disconnect between scientific focus and conservation needs is still present, with greater attention devoted to areas or taxa less rich in biodiversity and threatened biodiversity. In particular, a strong geographical bias persists, with 40% of studies carried out in USA, Australia or the UK, and only 10% and 6% respectively in Africa or South East Asia. Despite some changing trends, global conservation science is still poorly aligned with biodiversity distribution and conservation priorities, especially in relation to threatened species. To overcome the biases identified here, scientists, funding agencies and journals must prioritise research adaptively, based on biodiversity conservation needs. Conservation depends on policy makers and practitioners for success, and scientists should actively provide those who make decisions with the knowledge that best addresses their needs.
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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 | 0.167 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".