The Saskatchewan Spring of 2017: 34 Days that Shook the Province and Led to the Provincial Government Reinstating Funding to Public Libraries
Bibliographic record
Abstract
On March 22, 2017, the Government of Saskatchewan tabled its budget for fiscal year 2017-2018. There were deep cuts in many sectors. One of the biggest budget cuts percentage-wise was to public libraries. The Ministry of Education announced that 100% of operating grant funding ($1.3 million) to the province’s two largest municipal library systems (Regina and Saskatoon) would be eliminated. Additionally, seven of eight regional library systems would have a 58% reduction of $3.5 million in operating funding from the government (Ministry of Education, 2017). Reaction to the news was immediate and support within and outside Saskatchewan grew quickly to have the decision reversed. This article describes the incredible series of events made by the general public and key stakeholders in the library community that led to the Government of Saskatchewan reinstating all funding for 2017-2018 to public libraries 34 days after the budget was tabled.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 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".