Social Violence, Structural Violence, Hate, and the Trauma Surgeon
Bibliographic record
Abstract
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".