Fraser Forum Asking the Right Questions About Climate Change & the Kyoto Protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it