Amina Weira’s Anger in the Wind: A premonitory tale of intertwined subjectivities
Bibliographic record
Abstract
This article analyses Amina Weira’s Anger in the Wind (2016). It argues that Weira’s documentary tells the story of slowly dying men through an exploration of a failed Anthropocene mediated by subjectivity and memory. Behind an almost casual handling of the camera, Weira casts a keen sympathetic eye on the ‘environmentally embattled’ populations of her native town, Arlit. Anger in the Wind is a documentary of painful, angry, recriminatory words; it tactfully yet pointedly exposes the devastation of the local ecosystems by staging the life stories of men and women who now realize that they were seen as disposable entities, not worthy of a dignified life cycle, fit for sacrifice at the altar of western technological prowess and comfort. As much as a testimony, Anger in the Wind is a Bamako style indictment of a destructive way of inhabiting the earth and a Hyenas’ style call to collective resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".