Telling Tales over Time: Constructing and Deconstructing the School Calendar
Bibliographic record
Abstract
The September-to-june school calendar has been a fixture of North America for almost a century. Its origins have usually been told as an unexamined tale attributed to features of nineteenth century rural society. We challenge this interpretation by suggesting that multiple pressures arising from increasing urbanization influenced its roots. We present information on the importance of the summer holiday in the development of compulsory schooling in several North American jurisdictions, with the main evidence from Ontario, the most populous province in Canada. We suggest, along with Gold (2002), that this development had wider applicability in several Northeastern and Midwestern American states. Beyond the issue of having an accurate story line, we examine why there has been such resistance in recent times to changing the school year. The school calendar may be another example of an enduring institutional form referred to by Tyack and Tobin as a “grammar of schooling” that resisted fundamental change in the twentieth century. Viewing the school calendar's ties with changes over time in the construction of other clocks of society may enable us to rethink the format of the contemporary school calendar. Finally, we consider the school calendar as part of a larger, ongoing discussion of what constitutes effectiveness of schools.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".