Understanding the geography of Industry Canada's Community Access Program in Toronto
Bibliographic record
Abstract
Industry Canada’s Community Access Program (CAP) aims to provide affordable public access to the Internet and the skills that people need to use it effectively. In fact, the CAP is an Industry Canada effort to bridge the digital divide (rural-urban, intra-urban). In the City of Toronto Industry Canada funding is used to support CAP sites managed by two organizations, the Learning Enrichment Foundation and the Toronto District School Board. CAP was implemented through the establishment of community-based public Internet access facilities. The implementation of the CAP in Toronto has resulted in the use of a wide range of organizations and locations including: libraries, schools, community centres, employment and social service agencies, and language development centres. This research asks the question, is the current network of CAP locations adequately geographically organized to meet the demand for service provision? Adequate supply means that the neighbourhood CAP supply is not over-served and under-served. Data from Industry Canada’s CAP database and the Canada census are input to a modeling process that combines multi-attribute decision analysis with a location-allocation model. The results suggest that there is likely a need to reevaluate the geographical structure of the current CAP network, with a view to achieving a more equitable allocation of supply.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".