Black and Latinx hermeneutical resources, hip hop music and white supremacy
Bibliographic record
Abstract
Abstract I will argue that the diminishment of hip hop music as a hermeneutical resource for Black and Latinx persons by white supremacy promotes the ubiquity of ignorance of racial injustice in North America. To this end, I will defend what I call the hermeneutical-diminishment thesis. According to this thesis, white supremacy has diminished hip hop as a hermeneutical resource for Black and Latinx persons. To defend this thesis, I will substantiate two sub-theses. The first is the prescriptive-rap sub-thesis. According to this sub-thesis, white supremacy has caused prescriptive rap to predominate the content of hip hop in comparison to descriptive rap. The second sub-thesis is the descriptive-rap sub-thesis. According to this sub-thesis, if (i) hip hop can have prescriptive or descriptive content; (ii) hip hop with prescriptive content is a poorer hermeneutical resource for Black and Latinx people; and (iii) hip hop is dominated by rap music with prescriptive content; then (iv) hip hop is a poorer hermeneutical resource for Black and Latinx people. The defense that I present of the hermeneutical-diminishment thesis will take the following form. If the prescriptive-rap sub-thesis is true and the descriptive-rap sub-thesis is true, then the hermeneutical-diminishment thesis is true. I here show that both sub-theses are true, and therefore the hermeneutical-diminishment thesis is true.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".