Bibliographic record
Abstract
What is the purpose of public education? What is the value of taxpayer supported public schools? Who is invited to answer these questions? Except among policymakers, few publicly answer or debate these questions. Instead, the neoliberal forces of competition and deregulation seem to be driving education decision-making. The formal education system is seen as a tool for personal and national economic growth. Much of the education policy debate is centered on how to attain academic success as measured by standardized high stakes tests and evaluations. But, how to educate children and youth is a second order question. The first question must be ‘what is the purpose of schooling, and is it limited to the presumed answer that it is to prepare workers so our nations can sustain economic superiority?’ Students, parents, teachers, business people, artists, retirees, First Nations people, military veterans, and religious professionals are not typically invited to answer these questions – despite their stake in educational outcomes. Twenty-four such people, including professional educational policy makers and scholars, offer their thoughts in these essays from the US and Canada. The intended audience for this volume includes all who are concerned with the future of public schools in both nations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".