Bibliographic record
Abstract
Drought, floods, hurricanes, forest fires, ice storms, blackouts, dwindling fish stocks...what Canadian has not experienced one of these or more, or heard about the “greenhouse” effect, and not wondered what is happening to our climate? Yet most of us have a poor understanding of this extremely important issue, and need better, reliable scientific information. Hard Choices: Climate Change in Canada delivers some hard facts to help us make some of those hard choices. This new collection of essays by leading Canadian scientists, engineers, social scientists, and humanists offers an overview and assessment of climate change and its impacts on Canada from physical, social, technological, economic, political, and ethical / religious perspectives. Interpreting and summarizing the large and complex literatures from each of these disciplines, the book offers a multidisciplinary approach to the challenges we face in Canada. Special attention is given to Canada’s response to the Kyoto Protocol, as well as an assessment of the overall adequacy of Kyoto as a response to the global challenge of climate change. Hard Choices fills a gap in available books which provide readers with reliable information on climate change and its impacts that are specific to Canada. While written for the general reader, it is also well suited for use as an undergraduate text in environmental studies courses.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.197 | 0.066 |
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".