Bibliographic record
Abstract
Violence is an inescapable through-line across the experiences of institutional residents regardless of facility type, historical period, regional location, government or staff in power, or type of population. Population Control explores the relational conditions that give rise to institutional violence – whether in residential schools, internment camps, or correctional or psychiatric facilities. This violence is not dependent on any particular space, but on underlying patterns of institutionalization that can spill over into community settings even as Canada closes many of its large-scale facilities. Contributors to the collection argue that there is a logic across community settings that claim to provide care for unruly populations: a logic of institutional violence, which involves a deep entanglement of both loathing and care. This loathing signals a devaluation of the institutionalized and leaves certain populations vulnerable to state intervention under the guise of care. When that offer of care is polluted by loathing, however, there comes along with it an unavoidable and socially prescribed violence. Offering a series of case studies in the Canadian context – from historical asylums and laundries for “fallen women” to contemporary prisons, group homes, and emergency shelters – Population Control understands institutional violence as a unique and predictable social phenomenon, and makes inroads toward preventing its reoccurrence.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.207 | 0.044 |
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".