Bibliographic record
Abstract
The twenty-first century is a period of great environmental and social transformation as climate change increasingly marks lives at levels that are personal, familial, communal, national, and global. A Canadian Climate of Mind presents stories that emerge from the waters, lands, and climate of Canada, and which have the potential to renew a compassionate energy for changing human relations with each other and with our world. The turbulent effects of climate change are popularly discussed in the modern language of scientific knowledge, political policies, economic mechanisms, and technological innovation. While there is much to be learned from these views, Timothy Leduc suggests a more profound call for change by returning to past understandings of the land and climate. He argues that the world is initiating us into a broader and humbler sense of what it is to be human in an interconnected reality. The world is doing this by responding to unsustainable practices such as our devastating reliance on fossil fuels. Weaving together voices from numerous backgrounds and time periods with Indigenous views on present and past environmental challenges, A Canadian Climate of Mind illuminates a world that is being shaken to its core while we hesitate to act.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 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 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".