Bibliographic record
Abstract
When considering difficult societal issues it is quite common to suggest that those issues can be addressed through schools and developing related educational resources suitable to the task.For example, if social media is providing misinformation that negatively impacts youth, educators can develop curriculum in critical media literacy that supports critical youth engagement.If there is increased violence towards gender diverse community members, educators can develop sexual orientation/gender identity curriculum that educates students on respecting non-binary understandings of gender.What is often missing in these approaches is the notion that schools are not only places where societal inequalities might be addressed, but they are also the places that (re)produce them.In this case, when the inequalities are being (re)produced in the schools, how might we consider repair of educational settings themselves?The notion of repair may suggest that schools as we know them in Canada are repairable -that we can recognize and repair schools and their systems.This is certainly a matter up for some debate.As Vanessa Andreotti (2012) asks in relation to addressing issues of social justice through education: "How can one ethically and professionally address the hegemony, ethnocentrism, ahistoricism, depoliticization, paternalism and deficit theorization of difference that abound in educational
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.066 | 0.056 |
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".