Should Religious Schools Be Publicly Funded? Issues of Religion, Discrimination, and Equity
Bibliographic record
Abstract
This issue offers a critical opportunity to reflect on an enduring question in education: Should religious schools be state-funded? To facilitate this reflection, this issue offers six studies from Canada, Spain, and the United States. Each delves into the unique relationships between state-funded schooling and religion in their respective contexts. In particular, these studies examine how the relationships have shifted due to numerous factors, including changing legal rulings, political ideology, demographic shifts, global migration, and education privatization. The authors carefully integrate (and interrogate) the histories and places where they conducted their analyses. Taken together, these studies offer invaluable and timely insights into the intended and unintended consequences of state funding that expands school choice, marketization, and privatization, particularly with respect to religion. This issue thus aims to inform the ongoing debate about the (potential) impact that publicly funding religious schools has on equity, segregation, and discrimination. Ultimately, we hope this issue highlights the importance of a nonsectarian approach to public education so as to create an inclusive education space wherein all human identities are welcomed and affirmed.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".