Bibliographic record
Abstract
The stories told in this collection, though tragic for many, illustrate the steadfast determination and courage of people in the face of misfortune and extreme distress. From the lesser-known weed outbreaks and tornadoes to the world-wide influenza outbreak in 1918 that devastated many Calgary families, these stories focus on the human side of these disasters. It may be a heroic individual or the collective response of a community, but what is truly remarkable in these stories is the human response to the world being turned upside down by famine and disease, by flood, fire, or rock slide, by wind and cold, by dynamite or gas explosions, or even by the seemingly mundane threat of weeds upon crops. It is the resolution to continue to fight and the persistence of the human spirit and its adaptability to challenges that is the true story of a century of development in western Canada. With Contributions By: David Breen Patrick H. Brennan J.M Bumsted Joe Cherwinski Hugh A. Dempsey Janice Dickin Clint Evans Lorry W. Felske Max Foran David C. Jones Anthony Rasporich
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".