Fraser Forum Asking the Right Questions About Climate Change & the Kyoto Protocol
Bibliographic record
Abstract
Let us begin by dispensing with the wrong questions about climate change. Example: What do the world’s scientists say about global warming? (They will tell you not to assume that “the world’s scientists ” all agree on complicated issues and that enforced groupthink is fatal to scientific progress.) Example: We had a warm November and December—is this a sign of global warming? (No more than last year’s record cold November and December was a sign of a coming ice age.) Example: How much are we prepared to pay to save the planet from destruction? (“Saving ” or “destroying ” the planet is beyond our capabilities.) What, then, are the right questions? I propose the following: 1) Are infrared-absorptive gases (IRAGs) causing climate change? 2) Is the current climate change process harmful? 3) If so, will the Kyoto Protocol solve the problem?
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.031 | 0.015 |
| Insufficient payload (model declined to judge) | 0.130 | 0.063 |
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".