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Record W4214736305 · doi:10.1353/cro.2021.0042

Muslims in these United States: Living up to the Ideals of the Greatest in the Shadow of Terrorism

2021· article· en· W4214736305 on OpenAlexaboutno aff
Amir Hussain

Bibliographic record

VenueCrossCurrents · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsIslamHEROTerrorismState (computer science)Religious studiesAllegianceShadow (psychology)Muslim worldLawImmigrationAncient historyHistorySociologyPoliticsPolitical scienceArtPhilosophyLiteraturePsychology

Abstract

fetched live from OpenAlex

Muslims in these United StatesLiving up to the Ideals of the Greatest in the Shadow of Terrorism Amir Hussain (bio) Keywords Immigration, Islam in the U.S., Muhammad Ali, terrorism On the evening of June 11, 2016, I went to bed in Los Angeles after watching a replay of the funeral of Muhammad Ali, which had taken place the day before. I woke up the next morning to news of a mass shooting at the Pulse nightclub in Orlando. News reports indicated that the shooter was a private security guard who had sworn allegiance to the Islamic State of Iraq and Syria and claimed to be acting in retaliation for the death of one of the group's leaders, Abu Waheeb. These are the opposite poles of Muslim life in these United States. One is the death of my boyhood hero, perhaps the most famous person in the world, and an American Muslim. In death, Ali was beloved, given the equivalent of a state funeral. However, in his younger days, just after joining the Nation of Islam and changing his name from Cassius Clay to Muhammad Ali, he was reviled. It's that same revulsion that is often seen in the present day to Muslim terrorists. That's the other pole: an American Muslim who murders people in this country. I think of those two poles as I reflect on both my academic work and my life as an American Muslim during the past 25 years.1 I grew up with Ali as a hero and watched his transformation from loudmouth to elder statesman in the public consciousness. And since the terrorist attacks of September 11, 2001, I have been called on to talk about the issues of terrorism and violence among Muslims. Like many Americans, I am an immigrant to this country. I was born in Pakistan and arrived in Canada in 1970 when I was 4. At that time, there were fewer than 34,000 Muslims in all of Canada. I grew up in Toronto and was educated there, from kindergarten to the time of [End Page 360] earning my PhD. My parents and their generation were not pioneers of Islam in Canada. The first Canadian census in 1871 (the modern country came into existence in 1867) listed thirteen Muslims. But when my parents came to Toronto, there was only one mosque in the city and but one small store that sold halal meat. One of my mother's oldest friends told me that she met my mother around 1972, when my mother crossed a major city street because she heard this woman and her husband speaking Punjabi. My mother was so excited to hear a familiar language that she crossed a busy street to talk with strangers. Since then, the number of Muslims in Canada has grown tremendously; by 2001, there were 579,600, and in 2011, the most recent Canadian Household Survey, one million. Nowadays, it seems that one hears Urdu spoken everywhere, and the CBC broadcasts a Punjabi version of Hockey Night in Canada, something that would have been unimaginable in 1970. As a child, I never imagined that I would move to the United States, nor that I would become a scholar of Islam in North America. Growing up in the 1970s, racism was much more common, and I saw very few non-white people on television, let alone Muslims. The few I remember were Black athletes: Kareem Abdul-Jabbar, who had converted to Sunni Islam prior to his prodigious NBA career, and the Greatest of All Time, Muhammad Ali. Those were my childhood Muslim heroes, and over forty years later, they remain personal models for how to be a Muslim. At the age of 32, I moved to Los Angeles, where I have lived for the past twenty-five years. The American Muslims I observed were very different from those in European or Canadian Muslim communities, where we are also minorities in a Western context. Canadian Muslims do not have the same history as American Muslims. But the cultural dynamics are similar: at one pole, there are a select few Muslims who are beloved, and at the other, the reviled, anonymous Muslim...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.360
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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