Bibliographic record
Abstract
This chapter elucidates how movements, such as SlutWalk, translate into different cultural contexts by focusing on the Moroccan case. Clearly, SlutWalk has travelled to various cities highly distinct from Toronto: from Jakarta to Cape Town to Bangalore to Budapest to Turku to Rio de Janeiro to Kolkata to San Jose. To understand the question as to why Moroccan organisers changed the title of the movement, one should take inventory of why the name might be considered offensive, in which case, another important question remains as to why the organisers decided to remain connected to the global SlutWalk movement at its onset. This chapter answers these questions by examining which parts of SlutWalk were de/emphasised by Moroccan organisers who brought the movement to their country. The chapter describes how the term 'slut' does not easily accommodate social standards in conservative communities, whether western or non-western, lending to the controversy associated with the movement.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".