Bibliographic record
Abstract
Abstract On September 16, 2022, a young Kurdish Iranian woman, Jina Amini, or as she is internationally known, Mahsa Amini, died in hospital under police custody. Her death angered many Iranians but especially the Kurdish community who have been historically ostracized in the country pre- and post-1979 revolution. Jina’s death became the beginning of a robust feminist movement in the country called “Woman, Life, Freedom.” What this feminist movement represents, however, is not a domestic fight against a totalitarian regime but a universal struggle against intersectional oppression. Within this climate, many Iranian women music educators seek musical and educational opportunities outside of the borders, as they have been suffering from the absence of stable educational and musical freedom in the country. Higher music education institutions are some of the spaces from which these women music educators request assistance. In this chapter, I put forth two intermingling thoughts. First, I suggest regardless of the magnitude of the work, solidarity work in higher music institutions is not a choice, but a responsibility. Second, through the “Woman, Life, Freedom” movement in Iran, I propose that no solidarity work can happen without a feminist lens and even more so without being an affective organization of multiple forces. Solidarity work is necessarily a public and affective act. Specifically, within higher music education institutions one must consider this work as a collective and public endeavor.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.789 | 0.638 |
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".