Multiplying spaces of subalterity in education: From ideological realms to strategizing outcomes
Bibliographic record
Abstract
In recent years, the terrain of subalterity in education has multiplied in heterogeneous ways accentuated through the project of neoliberalism. Unpacking these socially, politically, or ideologically—through three contradictory imperatives—reveals intersecting spaces of marginality, hegemonic discourses, and complicated outcomes related to the governmentality of educational rights. However, despite serious efforts, the relentless task of contesting political rationalities, “normed” subjectivities, or technologies of power has not gained sufficient collective momentum to usurp such modes of governance and thought. These arguments are explored through a range of empirical cases in Ontario ranging from refugee and migrant rights, to the reproduction of racial/sexual/classed profiling and subjectivities, to territorial disputes related to northern and smaller school board challenges related to school closures. It is argued that the fundamental issue is not the dilemma related to redistribution and recognition rights, but the tensions strategically created through the manipulation of economic and moral orderings and consequential fragmentations of space and social justice.
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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.023 | 0.124 |
| Scholarly communication | 0.037 | 0.023 |
| Open science | 0.002 | 0.036 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".