Telling Tales Over Time: Calendars, Clocks, and School Effectiveness
Bibliographic record
Abstract
How do calendars and clocks influence considerations of school effectiveness? From the creation of compulsory education to the future of virtual schooling, Weiss and Brown trace two centuries of school practices, policies and research linking the concept of time with ‘opportunity to learn’. School calendars and clocks are shaped by both the physical and social worlds, and the ‘clock of schooling’ is shown to be one of the ‘great clocks of society’ that helps to frame school effectiveness. School time does not operate in a vacuum, but within curriculum, teaching and learning situations. The phrase ‘chrono-curriculum’ was devised by the authors as a metaphor for exploring issues of school effectiveness within the time dimension. Using American and Canadian sources, stories are created to illustrate four themes about time and school effectiveness. The first three stories utilize access, attendance and testing as criteria associated with these eras of schooling. How will the story read in the fourth era, the digital age, which forces us to a reconsideration of time and its influence on education? Quoting David Berliner in his Foreword: “ this is an opportune time for these authors to bring us insights into the reasons we in North America created our public school systems, and how the chrono-curriculum influences those systems. The authors’ presentation of our educational past provides educators a chance to think anew about how we might do schooling in our own times.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".