Configuring the field of global marine biodiversity conservation
Bibliographic record
Abstract
Introduction The article describes and analyzes the emergence of the field of global marine biodiversity conservation over the past fifteen years. We draw on collaborative research at international meetings, which we position as ‘field’ sites, places where diverse actors come together to negotiate the meaning and terms of global environmental governance and where that work is accessible and visible to researchers. Methods Based on Collaborative Event Ethnography (CEE), a method developed to facilitate study of large meetings, we mobilize research from seven meetings since 2008 to describe the field of global marine biodiversity conservation, but more importantly to specifying how that field has been configured. Results We identify practices of orchestration, narrative, performance, alliance, social objects, devices, and technologies, formal outcomes, and formal procedures, and their use at three phases of field configuration: building, framing, and bounding. Discussion The results: 1) enhance our understanding of the role of international conferences in global environmental governance generally, and for marine biodiversity conservation specifically; 2) demonstrate the relevance of field and field configuration theory; 3) contribute to theory on institutional fields by specifying practices of field configuration.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".