We’re Small Enough to Close but Big Enough to Divide: The Complexities of the Nova Scotia School Review Process
Bibliographic record
Abstract
Through interviews conducted in the fall of 2013 and winter of 2014, this paper presents a portrait of the various issues faced by community activists in fighting to keep their small rural schools open amidst constraints, most notably, provincial budget cuts and low enrolment numbers in rural areas. At the same time, school board members seeking to close small schools in rural areas faced their own sets of constraints. Participants were asked to discuss: their experiences in the small schools review process, their suggestions for policy design and implementation, and their notions around what small schools mean to rural sustainability and future economic development. Throughout these interviews, the participants from both contexts highlighted the struggles they faced during the review process and the impact of school closures on their children, their communities, and themselves. In addition to metrocentric (Green & Corbett, 2013) assumptions faced by the activists in the school review and closure process, there were additional issues concerning the configurations of people with different orientations as they attempted to participate in a democratic dialogue within the school closure process.
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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 | 0.194 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.048 | 0.028 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".