Bibliographic record
Abstract
In vernacular understandings or conversations about resistance as it occurs with students in public schools, it is frequently viewed as a negative action or set of behaviours to be changed or curtailed. This paper puts forward an argument that allows for the possibility of seeing moments of resistance as something to be recognized and celebrated with students. I do not suggest that resistance must always or only be viewed in this manner, but rather that it might be viewed thus, and thereby allow for multiple understandings of an action such as resistance. Beginning with my own hegemonic understanding of resistance as a necessarily bad/undesirable characteristic or behaviour, I then build on Foucault’s relational approach to understanding power (and its operations), leading then to a discussion of student engagement as also a relational process and one that has more than one possible form. By offering a possibility of understanding student engagement as a process that works both within and outside the structures that produce and maintain the White, middle class, heterosexual, abled, Christian, male child as the defacto subject of schooling, I build an argument that opens a more fluid conception of resistance than the necessarily negative action it is often perceived to be.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".