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Record W2954775897 · doi:10.1177/0020731419859834

Social Violence, Structural Violence, Hate, and the Trauma Surgeon

2019· article· en· W2954775897 on OpenAlexaff
Tanya L. Zakrison, Davel Milián Valdés, Carles Muntaner

Bibliographic record

VenueInternational Journal of Health Services · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsHarmCriminologyStructural violencePoliticsStructural inequalityEconomic JusticeAlienationPsychological interventionPopulationSocial issuesMedicinePsychologyPolitical scienceSociologyLawPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Violence can be committed against oneself or against another person or group. As trauma surgeons, we are often required to administer urgent surgical interventions on patients who have sustained life-threatening injuries, including from violence. The roots of such violence, nationally and globally, are related to structures of discrimination and alienation, termed “structural violence.” This is embedded in ubiquitous social structures and normalized by stable institutions and regular experience while “normalizing the abnormal.” Surgeons and physicians have a long history of critical analysis of the upstream “causes of the causes” to understand and prevent further harm. Hate can be well adapted to the classic public health model of the spread of “disease,” with hate speech as the vector leading to direct violence. Social medicine views social inequality as the cause of disease, with political action required to protect and improve population health. It acknowledges the need to address and end structural violence, through political solutions. It is our responsibility to create the dignified environments of growth and progress for our patients and to challenge the agents of harm such as hate and discrimination. As Dr. Norman Bethune, the father of social surgery stated, “Charity should be abolished, and replaced by justice.”

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.415
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2019
Admission routes1
Has abstractyes

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