Are fieldwork studies being relegated to second place in conservation science?
Why this work is in the frame
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Bibliographic record
Abstract
The collection of biological information, including data gathered in the field, is fundamental\n\t\t\t\t to improve our understanding of how human impacts on biological systems can be\n\t\t\t\t recognized, mitigated or averted. However, the role of empirical field research has faded\n\t\t\t\t appreciably in the past decades with sobering implications. Indeed, important instruments\n\t\t\t\t to help set national and global priorities in biodiversity conservation (i.e. synthetic analyses\n\t\t\t\t and big data approaches) can be severely handicapped by a lack of sound observational\n\t\t\t\t data, collected through fieldwork. We analyzed publication trends in the\n\t\t\t\t conservation literature from 1980 to 2014 to ascertain whether there is reason for concern\n\t\t\t\t about a potential decrease in fieldwork-based investigations compared to other types of\n\t\t\t\t studies. Here, we show that the proportion of fieldwork-based investigations in the conservation\n\t\t\t\t literature dropped significantly from the 1980s until today; indeed, fieldworkbased\n\t\t\t\t publications decreased by 20% in comparison to a rise of 600% and 800% in\n\t\t\t\t modelling and data analysis studies, respectively. In parallel, we found that the most highly\n\t\t\t\t cited academic journals in conservation science published fieldwork studies less\n\t\t\t\t frequently than the lower rank journals. We contend that an apparent decrease in\n\t\t\t\t fieldwork-based investigations is the result of bottom-up pressures, including those\n\t\t\t\t associated with the publishing and the academic reward systems, while a second set acts\n\t\t\t\t top-down, driven by current societal needs and/or priorities. We urge researchers, funders\n\t\t\t\t and journals to commit, respectively, to conducting, funding and divulging relevant\n\t\t\t\t fieldwork research, and make some recommendations on specific steps that can be\n\t\t\t\t adopted in that direction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it