Preface and Introduction - Religion and Climate Vol. 2 No. 1
Bibliographic record
Abstract
This issue of JCREOR goes back to a colloquium held on September 20th, 2019, at McGill University, organized by the Montreal based Council for Research on Religion, in which several leading McGill scholars and an interested audience discussed the many intersections between religion and climate change.Special attention was given to the question of how Eastern and Western traditions, as well as traditions from ancient times, can inform us on how to better respond -socially, politically, ethically, spiritually -to the effects of present-day climate change.The colloquium took place while a crowd of 500,000 people in Montreal, inspired by the Swedish teenage activist Greta Thunberg, demonstrated against climate change.Climate change involves not only catastrophic changes to the natural world in which we live, but also changes to our lives, our hopes and fears (increased anxiety, depression, doomsday feelings) as well as changes in society and politics (from new literary and film genres, to discussions about divesting, immigration politics, international relations etc.).Indeed, the present Covid-19 pandemic has clearly shown how everything and everyone in society is connected and intertwined, and that it is impossible to escape one's own responsibility.Let us all hope that the time for action has come now.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.170 | 0.064 |
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".