BOUNDARIES, BREACHES, AND BRIDGES: THE CASE OF CLIMATEGATE
Bibliographic record
Abstract
We examine the incident known as “Climategate” in which emails and other documents relating to climate scientists and their work were illegitimately accessed and posted to the Internet. The contents of the files prompted questions about the credibility of climate science and the legitimacy of some of the climate scientists’ practices. Multiple investigations unfolded to repair the boundary that had been breached. While exonerating the scientists of wrongdoing and endorsing the legitimacy of the consensus opinion, the investigating committees suggested revisions to some scientific practices. Despite this boundary repair work, the credibility and legitimacy of the scientific enterprise were not fully restored in the eyes of several stakeholders. We explore why this is the case, identify boundary bridging approaches to address these issues, and highlight policy implications.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.062 | 0.044 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.026 | 0.017 |
| 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".