Ready for School? Lessons from a Sociohistorical Investigation into Mechanisms of Preparation and Classification of Children for Primary School from 1911 to 1979
Bibliographic record
Abstract
In January 2013, the Quebec Ministry of Education announced the expansion of prekindergarten for four-year-olds. Half-day prekindergarten programs have existed since 1970 in the province, but only in “disadvantaged neighborhoods” within a limited number of school boards. The new, expanded system would implement full-day prekindergarten in at least one school within a “disadvantaged neighborhood” in every school board. This expansion was framed by the government as a crucial pillar in the fight against school dropouts, though the reasons for not offering this service to all children were not explained in official documents (MELS, 2013). The outcry against this policy focused on a debate about where children fare best: early childhood education (ECE) centers or prekindergarten classes, with arguments that ECE provides better quality, ratios, and developmentally appropriate practice than prekindergarten classes (CASIOPE, 2013; Duval & Bouchard, 2013; Moreau, 2013). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".