On the acceptance of intergenerational climate legacies: A comparison of Canada and Japan
Bibliographic record
Abstract
Intergenerational climate justice negotiations often flounder on three questions: What was the outcome on climate change? Was it intentional? Why should the current generation pay for the misdeeds of previous generations? In this research, participants from Japan and Canada rated their willingness to accept intergenerational climate legacies and the responsibilities these legacies entail; judged the importance of intent and outcome associated with creating these legacies; and rated their willingness to compensate those negatively impacted by previous generations. The study found: a) while outcome was important, intent did not matter; b) Canadians were more likely to accept an inheritance and c) more likely to equivocate, in acceptance, if it entailed obligations than the Japanese; d) among those who accepted the inheritance, Japanese were more generous in settlement of previous generation’s obligations; e) lower-income, non-Judeo-Christian participants were systematically fairer than others; and f) the resistance to compensation for past generations’ actions was diminished with the awareness about the broad scope of intergenerational climate legacies that the current generation enjoyed. Our findings highlight the influences of culture and historic awareness on accepting climate responsibilities for actions of previous generations and willingness to provide compensation. The findings also support abandoning the debate on intentionality.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".