Bibliographic record
Abstract
Abstract The education system of the province of Ontario, Canada ranks among the best in the world and has been touted as a model of excellence for other countries seeking to improve their education system. In a system-wide reform, leaders used a political and professional perspective to improve student performance on basic academic skills. The school system rose to renown after this reform which moved Ontario from a “good” system in 2000 to a “great” one between 2003 and 2010 (Mourshed M, Chijioke C, Barber M. How the world’s most improved school systems keep getting better, a report McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/social-sector/our-insights/how-the-worlds-most-improved-school-systems-keep-getting-better , (2010)). Premier Dalton McGuinty arrived in office in 2003 with education as his priority and was dubbed the “Education Premier” because of this mandate. His plan for reform had two primary goals: to improve student literacy and numeracy, and to increase secondary school graduation rates. McGuinty also wanted to rebuild public trust that had been damaged under the previous administration. The essential element of Ontario’s approach to education reform was allowing educators to develop their own plans for improvement. Giving responsibility and freedom to educators was critical in improving professional norms and accountability among teachers (Mourshed M, Chijioke C, Barber M. How the world’s most improved school systems keep getting better, a report McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/social-sector/our-insights/how-the-worlds-most-improved-school-systems-keep-getting-better , (2010)) and the sustained political leadership throughout the entire reform concluding in 2013 provided an extended trajectory for implementing and adjusting learning initiatives. The Ministry of Education’s Student Achievement Division, which was responsible for designing and implementing strategies for student success, took a flexible “learning as we go” attitude in which the reform strategy adapted and improved over time (Directions Evidence and Policy Research Group. The Ontario student achievement division student success strategy evidence of improvement study. Retrieved from http://www.edu.gov.on.ca/eng/research/EvidenceOfImprovementStudy.pdf , (2014)). This chapter will discuss influences on the reform design and key components of strategies to support student and teacher development and build a relationship of accountability and trust among teachers, the government and the public. The successes and shortcomings of this reform will be discussed in the context of their role in creating a foundation for the province’s next steps towards fostering twenty-first century competencies in classrooms.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".