Reducing publication delay to improve the efficiency and impact of conservation science
Bibliographic record
Abstract
Abstract Evidence-based decision making is most effective with comprehensive access to scientific studies. If studies face delays or barriers to being published, the useful information they contain may not reach decision-makers in a timely manner. This represents a potential problem for mission-oriented disciplines where access to the latest data is paramount to ensure effective actions are deployed. We sought to analyse the severity of publication delay in conservation science — a field that requires urgent action to prevent the loss of biodiversity. We used the Conservation Evidence database to assess the length of publication delay (time from finishing data collection to publication) in the literature that tests the effectiveness of conservation interventions. From 7,415 peer-reviewed and non-peer-reviewed studies of conservation interventions published over eleven decades, we find that the mean publication delay (time from completing data collection to publication) was 3.6 years and varied by conservation subject — a smaller delay was observed for studies focussed on the management of captive animals. Publication delay was significantly smaller for studies in the non-journal literature (typically non-peer-reviewed) compared to studies published in scientific journals. Although we found publication delay has marginally increased over time (1912-2020), this change was weak post-1980s. Publication delay also varied inconsistently between studies on species with different IUCN Red List statuses and there was little evidence that studies on more threatened species were subject to a smaller delay. We discuss the possible drivers of publication delay and present suggestions for scientists, funders, publishers, and practitioners to reduce the time taken to publish studies. Although our recommendations are aimed at conservation science, they are highly relevant to other mission-driven disciplines where the rapid dissemination of scientific findings is important.
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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 | 0.522 | 0.831 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".