Growing disparity in global conservation research capacity and its impact on biodiversity conservation
Bibliographic record
Abstract
Growing disparity in global conservation research capacity and its impact on biodiversity conservation Lu Zhang1,2#, Li Yang1#, Colin A. Chapman3,4,5,6, Carlos A. Peres7,8, Tien Ming Lee1,2*, & Peng-Fei Fan1* 1School of Life Sciences, Sun Yat-Sen University, Guangzhou, China 2School of Ecology, Sun Yat-Sen University, Shenzhen, China 3Woodrow Wilson International Center for Scholars,1300 Pennsylvania Avenue NW, Washington DC, USA 4Biology Department, Vancouver Island University, 900 Fifth Street, Nanaimo, British Columbia, Canada 5School of Life Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa 6The College of Life Sciences, Northwest University, Xi’an, China 7School of Environmental Sciences, University of East Anglia, Norwich, UK 8Instituto Juruá, Manaus, Brazil #These authors contributed equally to this work. *Correspondence: fanpf@mail.sysu.edu.cn; leetm@mail.sysu.edu.cn Abstract: To achieve the 2030 Agenda for Sustainable Development and the 2050 Vision for Biodiversity, the Kunming Declaration (COP-15) commits to increasing biodiversity conservation capacity in developing countries. Yet, a global evaluation of conservation research capacity (CRC) and its impact on biodiversity conservation is still lacking. Here, by analyzing over 177,000 scientific papers from major conservation journals published after 2000, we derived six indicators of CRC and monitored their changes for the 193 United Nations member countries. We found that while CRC expectedly varied globally, the disparity in CRC between the top- and bottom echelons grew over time. While most CRC indicators improved biodiversity conservation status (i.e., the IUCN Red List Index) in high-CRC countries, only the number of collaborating countries had a positive impact for low-CRC countries. Therefore, building CRC must be a top conservation priority and high-CRC countries must lend greater support for low-CRC countries through meaningful collaborations, and funding truly collaborative research in low-CRC developing countries. ########data info######### 1 readme.txt Detail info for Rdata, please read before you use our code 2 Rdata 3 code.zip Including three code files: 1) 0 calculate function exclude oversea.R basic infor for calculation 2) 1 extract data.R Fist step for data cleaning, more detail provided in the file 3) 2 extract research location.R Final step for data extraction. 4) Supplemental Tables Including 8 sheet in this table: Table S1. Correlation (Spearman rho) between indicators of conservation research capacity Table S2. Detailed information of the indicators of conservation research capacity for all the 193 countries Table S3. Contributing variables used to model RLI changes of sovereign countries Table S4. A sensitivity analysis to evaluate the effect of publication cutoff (and hence CRC class division) on the RLI change regression analysis Table S5. Language composition of publications obtained from the Scopus database, based on journals listed in the minor subject area of “Nature and Landscape Conservation” in Scopus (n=189), and a combined journal list includes those from Scopus and journals in the category of “Biodiversity Conservation” in the Web of Science Core Collection (n=229) Table S6. A combined list of journals used to collect publications for further analyses Table S7. Comparisons of contributing variables (Mean ± SD) between countries with high- and low conservation research capacity (CRC) Table S8. Correlation (Spearman rho) between contributing variables used to predict Red List Index change for all sovereign countries (n =181)
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".