Violence Beyond the Proximal Subjective: Theorizing an addendum of distal causality
Bibliographic record
Abstract
Not a day passes over society where the immediate expressions of violence are not widely propagated and subsequently witnessed through cultural or political mediums. Such depictions, accounts, scenes, have been uniquely framed as a lexis of ‘subjective violence’; reactions or evident illustrations of descent in physical form. Amidst their over-representation is a lapse of measured attention given to the pretext(s) amounting to said outbursts. Seldom is the objective, if at all, contextualized as a catalytic toward the subjective (violence). The following work suggests that a violence exists amidst society that goes substantively under-analyzed thereby negating an ability to specifically address and, therefore, challenge its causality. While recognizing the importance of such research, a movement beyond affectual approaches of theorizing violence is needed through a complimentarily mapping of how distal relations of power influence, impact, and sustain enmity. What makes this discussion further dynamic is the ironic transparency of causation. Rather than a phenomenon of concealment, conventional dynamics of authority and influence exert control through, what could be argued to be, an invisibility of visibility. Supporting an addendum to theorizing violence may, then, enable a more holistic recognition that can assist an articulate response toward both subjective and objective expressions of violence.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.058 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".