The Policy Shop: Innovation, Partnerships and Capacity-building
Bibliographic record
Abstract
Recently, the Canadian federal government has moved to an alternative service delivery model where the third sector has been increasingly called on to fill the gaps in government service delivery. However, these organisations suffer from tight budgets and burdensome accountability measures, and generally they do not have resources to undertake policy work. New organisations that promote innovation in the third sector are needed to fill this gap, so the Johnson-Shoyama Graduate School of Public Policy, located in Saskatoon, developed the Policy Shop, a student-run, pro-bono policy consultancy, to meet the needs of the third-sector organisations in the policy space. Organisations like the Policy Shop serve as innovation brokers and link third-sector organisations to the university–industry–government Triple Helix network.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".