Learning together out of climate change denial
Bibliographic record
Abstract
Abstract Climate change denial is often rooted in an array of conspiracy theories with climate change itself viewed by some as the real conspiracy. While not all of us are climate change conspiracy theorists, it is upsetting for most of us to learn and accept that our climate is changing, that it is primarily human‐caused, and that it is harming people, animals, and other living organisms. It is even harder to accept this new reality when it goes against our core and fundamental beliefs, and against the professed beliefs and positions of those groups with which we identify. The media—whether old or new—has not really helped educate, and has tended to reinforce what is termed ‘myside’ bias. Those of us in adult education know that presenting the correct information or facts is not enough to really teach people. Instead, I sense that learning to live together, to accept the painfulness of learning, and connecting in a shared concern for our fragile blue planet and what we have managed to create, needs to be part of the answer—as complex as this topic is.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".