Bibliographic record
Abstract
This article contributes to debates about subjectivity formation in neoliberal times by analyzing the emotional geographies of students’ imagined futures. Drawing upon ethnographic research in a white, working‐class rural Ontario school, I examine students’ participation in The Real Game, a career education program that attempts to prepare grade 7/8 students for their adult futures. A curricular tool that espouses neoliberal tenets of flexibility, mobility, and self‐improvement, The Real Game offers a site through which to explore the interplay between governing discourses and student subjectivities. Bringing Ahmed's critique of happiness narratives to an analysis of student interviews, I demonstrate how neoliberal governance operates affectively. As educational discourses idealize the self‐reliant, future‐oriented subject, students are trained to internalize neoliberal uncertainty as a set of insecurities to be managed on an affective level. The distinctly spatial operation of neoliberalism is apparent in Fieldsville students’ future narratives, where dominant ideals of mobility conflict with local identifications and an allegiance to place. Students manage these pressures affectively, as they narrate their own movement and improvement through stories of hope, fear, and wonder. Thus, I argue that studies of the emotional geographies of education are integral to understanding how neoliberalism is lived in place.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".