Bibliographic record
Abstract
The campaign to discredit predictions of man-made global warming—originally organized by readily identifiable vested interests—has by now recruited a large popular constituency of declared “skeptics” increasingly disposed to “take a stand”: some of them opposed to government regulation in general, some resistant to any claims to intellectual authority (perhaps especially scientific), and some mobilized by a version of the right to individual freedom of opinion. As a result, confidence in the expertise of scientists has reached an all time low: Internet sites, radio talk shows, and television channels preferentially transmit “contrarian” attacks on the credibility of climate scientists. Even our most responsible newspapers and journals, in their very commitment to the traditional ethic of “balance,” sometimes contribute to the widespread misimpression that climate scientists are deeply divided about both the extent of the dangers we face and the relevance of human activity to global warming. Not knowing who or what to believe, the natural response for most people is to do nothing, and the consequence, as Thomas Homer-Dixon wrote last year for the New York Times: “Climate policy is gridlocked, and there’s virtually no chance of a breakthrough” (2010). Meanwhile, as evidence both of the role of human contributions to global warming and the dangers of that warming continues to mount, consensus among climate scientists grows ever stronger, and those of us who attend to that evidence are increasingly alarmed.
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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".