Bibliographic record
Abstract
Based on our chapter from the State of Rural Canada Report, this presentation considers rural peoples and places across Alberta. This poster will present an overview of the population shifts, economic realities, and electoral politics in rural Alberta. With a population over 4.3 million, and growing (mostly in urban centers), Alberta is the fourth largest province in Canada. While rural Albertans continue to overwhelmingly vote conservatively, support for the conservative party has diminished and population shifts mean that rural Alberta no longer has the population base to determine election outcomes. Many of the challenges faced by rural municipalities are long-standing, but increasingly compounded by economic decline, provincial fiscal policy, deteriorating infrastructure, increasing urbanization, and aging populations. Complimenting this broad overview are two case studies focused on the impacts of COVID-19 in Canmore and the lasting impacts of the Fort McMurray wildfires. These are included to provide specific evidence of rural resiliency in the face of adversity. In conclusion, we discuss the diversity and complexity of identity, place and people in rural Alberta, drawing attention to the work rural Albertans are doing to protect the land and water, public services, and communities in which they are invested and rely on.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".