Why ‘Anti-Sharia’ Protestors in Los Angeles are Concerned about Muslim Women
Bibliographic record
Abstract
On a sunny Los Angeles afternoon in June of 2017, I was driving to the airport to pick up my cousin who was coming to visit me from Canada for the very first time. As I approached the international terminal I heard loud music interspersed with bursts of chanting and cheering – something that sounded like a protest. Having recently been hit by Donald Trump’s Muslim Ban, Los Angelinos of many races and faiths had taken defiantly to the streets against his punitive ban, and had flooded the international terminal at Los Angeles International Airport (LAX) in protest. Like many protests in Los Angeles that have followed Trump’s election, this protest was beautiful in demonstrating a multiculturalism grounded injustice for all and a vision of America that rejects divisive tactics of the state that single out one group as a ‘problem.’ As I circled around the bustling airport on that June afternoon, I called my sister to ask if she could check the news to see if there was a new trump policy that may have sparked further protests. After a moment of pause she said “Oh no, I heard on the news that today there are going to be anti-Sharia protests in 28 cities across the country. I hope that isn’t what this noise is about.”
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".