Accountability and the academy: producing knowledge about the human dimensions of climate change
Bibliographic record
Abstract
Calls for accountability and ‘impactful’ research are fundamentally reshaping the academy, giving rise to a large, critical scholarship on neoliberal regimes of accountability and their pernicious effects. But these calls also animate other institutional forms and practices that have received less critical attention. These include new forms of science that promise accountability through interdisciplinarity, collaborating with stakeholders, and addressing real‐world problems. This article considers one example of such accountable science: human dimensions of climate change field research. This research endeavour has produced surprising results, including the uncritical adoption of controversial Euro‐American ideas about traditional Others. In exploring how this has come about, the article considers how theoretical and disciplinary diversity are managed within this arena, and the organizing logics that enable climate sciences and scientists to work together. We ultimately argue that accountable science – like other neoliberal modes of accountability – can produce outcomes for which no one can be held to account.
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".