[Comment on “Raising the ante on the climate debate”] Wasting public money?
Bibliographic record
Abstract
The letters written by U.S. Rep. Joe Barton and his committee (Forum, Eos, 12 July 2005, p. 262) asking for data from Michael Mann and other scientists are certainly chilling because of the time it would take to meet the requests, but Rep. Barton has every right to make such requests. Thomas Crowley asked,“At some point, one must ask why should a member of the U.S. Congress get involved in this matter which may have been raised by a Canadian?” He opines that the reason was to intimidate and foment uncertainty. The real answer is, Barton is making the request because he can and is doing what, in his mind, is his job. That the issues were raised by a Canadian is immaterial; plenty of Americans are also skeptical of global change research. Moreover, the argument that “there is more agreement than disagreement” among the various paleoclimate reconstructions would not resonate with a layperson; even laypeople know that the history of science is fraught with examples of consensuses that proved wrong.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.057 | 0.044 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".