The “Normalized Quiet of Unseen Power”: Recognizing the Structural Violence of Deindustrialization as Loss
Bibliographic record
Abstract
This article explores the structural violence of deindustrialization and the urban losses that result. It is a global story of mass displacement and dispossession but also an intensely local one that has devastated the working-class. But much of this history is submerged under a dominant, postindustrial, discourse that instills not only a sense of inevitability but of progress and where the ravages of deindustrialization, when recognized at all, are safely contained to rust belt zones or inner-city areas. These twin processes of “invisibilization” can even co-exist within a metropolitan area like Montreal where deindustrialization’s lasting effects are at once too diffuse and too localized to be noticed, further privatizing the pain and hurt that results. In exploring the internalized despair produced by the structural violence of deindustrialization, the article invites us to consider the ways that public recognition or non-recognition structures the public conversation about what’s lost in mill or factory closings. Edward Said, among others, has asked us to interrogate the “normalized quiet of unseen power” when violence becomes largely invisible to us.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".