The Driver Education and Licensing Project at Sipekne’katik First Nation
Bibliographic record
Abstract
[...]there is no municipal bus service.2 Although Halifax Transit stops at the Robert Stanfield International Airport, that location is about 30 kilometres from SFN. [...]family and friends are pressed into service, or a local "taxi" can be engaged; but many residents cannot afford the cost. A year later, as we are nearing the end of the work with this group, another 102 adults have applied to be part of a second cohort - an unprecedented response for any initiative at SFN, and an indication of just how important the transportation issue is for community members. In addition to two Band Council members, the Committee includes the Native Employment Officer for SFN, the DELP coordinator, and an administrative trainee, and representation from organizations such as Mi'kmaq Legal Support Network, Mi'kmaq Employment and Training Secretariat, Dalhousie University, and the provincial Registry of Motor Vehicles, Court Services and Aboriginal Affairs. Financial support was obtained from the grant supporting the Poverty Action Research Project provided by the Canadian Institutes of Health Research and administered through Dalhousie University, as well as grants from the Nova Scotia Building Vibrant Communities Fund, the Native Council of Nova Scotia accessing funds from Service Canada, Mi'kmaq Employment and Training, and the Sipekne'katik
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".