Mad matters : a critical reader in Canadian mad studies
Bibliographic record
Abstract
Preface Introducing Mad Studies The Movement Women in 19th Century Asylums: Three Exemplary Women -- A New Brunswick Hero Democracy Is a Very Radical Idea What Makes Us a Community? Reflections on Building Solidarity in Anti-Sanist Praxis A Rose by Any Other Name: Naming & the Battle against Psychiatry Breaking open the bone: Storying, Sanism, & Mad Grief Mad as Hell: The Objectifying Experience of Symbolic Violence A Denial of Being: Psychiatrization as Epistemic Violence Mad Success: What Could Go Wrong When Psychiatry Employs Us as Peers? The Tragic Farce of Community Mental Health Care Electroshock: Torture as Treatment Is Mad Studies Emerging as a New Field of Inquiry? Making Madness Matter in Academic Practice Mad Patients as Legal Intervenors in Court Removing Civil Rights: How Dare We? They should not be allowed to do this to the homeless & mentally ill: Minimum Separation Distance Bylaws Reconsidered The Making & Marketing of Mental Health Literacy in Canada Pitching Mad: News Media & the Psychiatric Survivor Perspective Mad Nation? Thinking through Race, Class, & Mad Identity Politics Whither Indigenizing the Mad Movement? Theorizing the Social Relations of Race & Madness through Conviviality Spaces in Place: Negotiating Queer In/visibility within Psychiatric & Mental Health Service Settings Rerouting the Weeds: The Move from Criminalizing to Pathologizing Troubled in The Review of the Roots of Youth Violence Recovery: Progressive Paradigm or Neoliberal Smokescreen? Glossary of Terms References Case Law & Statutes.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.082 | 0.053 |
| Scholarly communication | 0.034 | 0.013 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".