Bibliographic record
Abstract
Liberal, capitalist societies such as Canada focus the treatment of stress and trauma on the individual. This is understandable given the roots of liberal ideology are embedded in individual sovereignty and the pursuit of happiness. While the impacts of stress and trauma can be severe on the individual, the focus on individual treatment diverts attention from the structural origins of traumatic events and their enduring circumstances. The chapter focuses on these origins and the state as an instrument of violence. It explores how capitalism, patriarchy, and racial supremacy have fueled not simply individual traumas but collective traumas, including the impacts of imperialism, cultural and racial genocide, the occupation and seizure of lands, environmental destruction, the oppression of women, and the entrenchment of poverty. The chapter is underpinned by Macpherson&s;s analysis of liberal democracy, Chomsky&s;s postulate of the state as a violent institution, and Freud&s;s exploration of happiness. The chapter draws on other neo-Marxist, feminist, and critical theorists to argue why we must understand trauma as a collective, as well as an individual, experience. Liberal societies, it is argued, will only alleviate trauma when they abandon notions of individualism and embrace collective welfare as the path to happiness.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".