Publishing social science research in <i>Conservation Biology</i> to move beyond biology
Bibliographic record
Abstract
Conservation biology arose as a field of academic science and management practice to intervene in what biologists and related professionals identified and perceived as a biodiversity extinction crisis (Soulé 1985). Although it has earlier foundations (e.g., Biological Conservation began to be published in 1968), the new discipline was consolidated in the 1980s and 1990s under the leadership of a group of eminent biologists, who institutionalized this paradigm via their seminal writings, with the creation of the Society for Conservation Biology in 1987, the founding of the journal Conservation Biology in 1988, and the proliferation in the number of conservation biology graduate programs during the early 1990s (Meine et al. 2006). Initially focused on critical biological aspects of conservation, such as genetics, systematics, ecology, and evolution, conservation biology professionals increasingly recognized that the human dimensions of biodiversity are requisite components to the field's overall success (Meine et al. 2006). However, given its personal, epistemological, and institutional roots in the natural sciences, less attention has been paid to the social aspects until relatively recently (e.g., see Fig. 1 in Soulé [1985], Mascia et al. 2003).
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.104 | 0.031 |
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".