Bibliographic record
Abstract
As if to give the lie to my last editorial, in which I argued that the “war onterror” was a smokescreen covering the imperial ambitions of the UnitedStates’ neo-conservative political elite, on the day that the issue went to press,Toronto’s Royal Canadian Mounted Police (RCMP) announced the arrest ofseventeen young Muslim men on terrorism-related charges. Five are under18, several are over 30, and the rest are in their late teens and early 20s.The shock permeated Toronto. Non-Muslims were shocked that “itcould happen here,” and Muslims were shocked that some of their own werewilling to kill fellow Canadians. As can be imagined, the following mediafrenzy displayed the usual racism (disguised as attacks on multiculturalism)from commentaries, editorials, letters to the editor, and experts concerningthe “threat” of Muslim extremism in Canada. Muslim organizations andthose with links to the media were back on the media circuit (or is it circus?)doing interviews, hot on the heels of the cartoon controversy, trying toexplain this and to distance themselves and Islam as a religion from attack.There was the usual spike in Islamophobic backlash, although this waslargely contained by Toronto’s Mayor David Miller and other leaders.There was also the usual skepticism and claims of anti-Muslim discriminationfrom some Muslims. While we do not know the veracity of the evidence,and while it may be admirable that the belief is so strong that Islamprohibits terror that we cannot conceive of fellow Muslims doing such athing, it ultimately harms the community that this kind of response is sowidespread. For one thing, the media use this sentiment to mock us and portrayus as cold and indifferent to the threat of terror. For another, although itseems to be painful for some to admit, our community has to take ownershipof the extremism existing in its midst.These men may be innocent and may have been framed or discriminatedagainst, but we have to face up to the results of such extremist interpretations.It is all very well to say that “this is not Islam” and to worry aboutthe media’s portrayal of Islam as a religion of violence, but we must also talkto ourselves and our youths and show them that such actions are beyond thepale of Islam. Moreover, we need to debunk the arguments of those Muslimswho challenge this view ...
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.368 | 0.267 |
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".