Effects of parachute science on local research capacity 降落伞科学对当地研究能力的影响
Bibliographic record
Abstract
Strengthening research capacity is essential to address the global biodiversity crisis. Yet, parachute science often undermines this goal, and its prevalence, costs, and benefits are unclear. We analyzed 13,502 publications on primate research that we extracted from Scopus (1960-2022) to evaluate the effects of parachute science on local research capacity across primate-range countries. We categorized these publications as local (LRP), collaborative (CRP), or parachute science (PSRP) research publications and categorized countries where the research took place as low- to middle-income countries or high- to upper-middle-income countries. We used generalized linear mixed-effects models to assess how parachute science influenced local research capacity. For 69% of PSRPs, the research was conducted in 59 low- to middle-income countries. For 20% of LRPs, research was led by people from these 59 countries. The disparity in LRPs among country groups was large. Local research publications in high- to upper-middle-income countries were at least 3.6 times higher than those in low- to middle-income countries. Before 2013, parachute science contributed to an increase in LRPs; this trend reversed after 2013, mainly resulting in a decline in LRPs across all countries and both income categories. Strengthening the capacity to share research in low- to middle-income countries is urgent if international conservation commitments are to be met. We recommend establishing true collaborative and interdisciplinary research teams, expanding local research opportunities, and supporting long-term research projects as key strategies for sustainable research capacity strengthening in low-income countries.
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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 | 0.141 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.036 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".