Sisyphus and Climate Change: Educating in the Context of Tragedies of the Commons
Bibliographic record
Abstract
The tragedy of the commons is a primary contributing factor in ensuring that humanity makes no serious inroads in averting climate change. As a recent Canadian politician pointed out, we could shut down the Canadian economy tomorrow, and it would make no measurable difference in global greenhouse gas emissions. When coordinated effort is required, it would seem that doing the “right thing” alone is irrational: it will harm oneself with no positive consequences as a result. Such is the tragedy. And that is the challenge that we take up here. Though Garrett Hardin suggests that the solution is a governmental process that rules over all contenders, since a world government seems unlikely before the planet hits the tippy point, we suggest an educational initiative instead: one that holds a mirror up to the behaviour of individuals, rather than to the behaviour of individuals in groups. Such an educational initiative would be focused on priming individuals to keep constant track of what they do as individuals as opposed to focusing on the behaviour of humanity in general. Such an educational initiative would focus on tackling the “problem solvers” rather than just “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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".