A School Is Born: Correlates of Recent Births of Independent Schools in Ontario
Bibliographic record
Abstract
Ontario’s population of independent schools does not receive any public subsidies while having to compete with a public system that is well-funded, offers a relative abundance of choice, and performs well from an international perspective. Yet that population has grown steadily despite encountering those unfavorable conditions. To understand this growth, this paper examines correlates of independent school openings between 2011–12 and 2020–21. It draws on organizational ecology frameworks that are typically used to study for-profit businesses but are rarely used to examine schools. Data come from official school registries, coding school websites, and merging census information, and are used to operationalize 3 key concepts: carrying capacity, resource partitioning, and density dependence. Descriptive analyses, logistic regression models, and sensitivity checks suggest the following: new independent schools tend to emerge in International and “Third” sectors, at the secondary level, near other independent schools, and offer additional educational services. We interpret these findings as illustrating that internationalization is extending capacities for independent schools and is helping to enhance their organizational legitimacy, and that school characteristics are likely products of both period effects and organizational changes. Policy implications are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".