Mapping Deprivation for the Small City, Rural Context: a Kamloops–Thompson Case Study
Bibliographic record
Abstract
Deprivation Indices are tools of value to social service agencies planning for the effective delivery of community social programs. Social and economic factors are aggregated and mapped to determine which areas are more deprived. Deprivation differs from poverty in that it is based on social conditions rather than income; to be deprived is to live below socially accepted standards of living. The geography of this study is defined by census dissemination areas within the Kamloops – Thompson School District 73. Reflecting on the Canadian small city and rural context, this study adapted deprivation index formulas from other indices in current literature. It is anticipated that this methodology will be transferable to other Canadian communities. The maps created are intended to support decision making on program delivery by local social service and sustainability groups. This study is an outcome of a research partnership comprised of university, community organizations, and government agencies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.866 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".