Global environmental agreement-making: Upping the methodological and ethical stakes of studying negotiations
Bibliographic record
Abstract
This perspective identifies how recent advances contribute to re-evaluating and re-constructing global environmental negotiations as a research object by calling into question who constitutes an actor and what constitutes a site of agreement formation. Building on this scholarship, we offer the term agreement-making to facilitate further methodological and ethical reflection. The term agreement-making broadens the conceptualisation of the actors, sites and processes constitutive of global environmental agreements and brings to the fore how these are shaped by, reflect and have the potential to re-make or transform the intertwined global order of social, political and economic relations. Agreement-making situates research within these processes, and we suggest that enhancing the methodological diversity and practical utility is a potential avenue for challenging the reproduction of academic dominance. We highlight how COVID-19 requires further adapting research practices and offers an opportunity to question whether we need to be physically present to provide critical insight, analysis and support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.109 |
| Scholarly communication | 0.027 | 0.045 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".