Bibliographic record
Abstract
Small cities face intricate challenges. No Straight Lines provides the basis for a refined model of community-engaged leadership and research designed to realize equality of quality of life. With particular attention to the small city of Kamloops, BC, this book explores the impact of extended, short-term, and unique leadership collaborations and local responses to homelessness, sustainability and food security, aging populations, and the recovery of local history. It offers exciting insights into the role of the university in the small city, from generating local learning opportunities to the integration of undergraduates and faculty in achieving positive change. Based on active engagement, No Straight Lines reveals the obstacles present in addressing local needs, and the transformations that can be achieved through effective collaboration. It offers rich accounts and valuable insights into flexible practices that respond to the needs of community organizations while recognizing the challenges associated with resource constraints and limitations in capacity. This unique collection provides new insights into the barriers and benefits of leadership and learning in the small city. With Contributions By: Ginny Rastoy, Lisa Cooke, Robin Reid, Kendra Besanger, Dawn Farough, Tina Block, and Terry Kading
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.918 | 0.923 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".