Exploring Rural Precarious Employment: The Case ofOntario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This panel will provide an overview of the first year and half of this this three-year research project. The panel will be divided into 3 presentations, each presentation pertaining to a unique part of the project, delivered by a different research team member. i. Precarious employment: What it is and what it means for rural?In this presentation, an overview of precarious employment will be provided, including a conceptual description of precarious employment, a summary of empirical findings noting its impact upon individuals, families and communities. The presentation will finish with examining the implications for rural Ontario. ii. Precarious employment in non-metro Ontario: A statistical overviewIn this presentation, a statistical overview will be provided of rural precarious employment. It will begin by comparing key precarious employment indicator trends for metropolitan census division, partially-non-metropolitan census division and non-metropolitan census division for Ontario. This will be followed by a presentation of trends of key indicators of precarious employment as it relates to non-metropolitan census divisions. An aggregated presentation of key indicators for individual Ontario regions will also be provided. iii. Precarious employment on the ground: Stories from rural Ontario In this presentation, a reporting back on key informants (professionals working first hand with those who have experienced or are experiencing rural precarious employments). Topics covered include pervasiveness of precarious rural employment, impacts, mitigation and current supports that help those rural people who are precariously employed manage their employment situation or escape it.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it