Sociologies of climate change are not enough. Putting the global biodiversity crisis on the sociological agenda
Bibliographic record
Abstract
[Extract] In December 2022, the 15th meeting of the Conference of the Parties (COP15) to the UN Convention on Biological Diversity adopted what was described in the official press release as a ‘historic package of measures deemed critical to addressing the dangerous loss of biodiversity and restoring natural ecosystems’ (CBD Citation2022). These included protection of at least 30% of the world’s lands, inland waters, coastal areas, and oceans by 2030 (thereby endorsing the ‘global deal for nature’ or 30 × 30 initiative proposed by Dinerstein et al. Citation2019) along with restoration complete or under way on at least 30% of degraded terrestrial and aquatic ecosystems and a suite of other goals and targets. I will outline these in a little more detail below. However, my aim in this essay is not to provide a comprehensive overview of the agreed Kunming-Montreal Global Biodiversity Framework but to consider its implications for sociology and cognate social sciences – to ask how signing of this ‘landmark agreement’ might inform research agendas, and the practical contribution of sociology to more just and sustainable futures.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads 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".