Bibliographic record
Abstract
n the last decade or so there has been a fervent public interest in educational issues, prac-tices and beliefs. Public debates in Canadian media have focused on such issues as mainstreaming, whole language vs. phonics, separate schools, multiculturalism and anti-racism in schools, accountability, standardized testing, teacher testing, and concern about educational stan-dards. The call for common national standards, the arguments to evaluate more thoroughly stu-dents ’ achievements, as well as the complaints about the lowering of standards in schools, demon-strate the need to seriously examine the issue of standards which has become so predominant in educational debate. In this paper we first clarify the notion of standards. We claim that common yet serious misinterpretations arise from confusions about the meaning of the concept of standards in popu-lar discourse. Second, we offer a critical examination of the assumptions underlying popular dis-course about standards; and finally, we offer alternative perspectives on educational standards and justification for these perspectives. These alternative perspectives rest on the conception of the «curriculum of life » – a curriculum that is grounded in the immediate daily world of students as well as in the larger social political contexts of their lives. It will be argued that it would be more worth-
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 | 0.006 | 0.009 |
| 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 | 0.042 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".