Serving a Country That Will Not Accommodate Our Religion: The Sikh American Struggle to Choose Between Career or Faith
Bibliographic record
Abstract
Sikhism is the fifth largest religion in the world, with approximately thirty million followers of the faith worldwide. It is a monotheistic faith that teaches honesty, compassion, humility, universal equity, and respect for all religions. Since the 1984 genocide of Sikhs in India, many followers of the faith have immigrated to Western countries in hopes of “the American dream” and the prospect of freely practicing their faith. But as a devastating response to the tragedy of 9/11, members of the Sikh community living in the United States have become victims of hate crimes, workplace discrimination, school bullying, and racial and religious profiling. As a scholar of the traditions and a practicing Sikh myself, I have learned the harsh realities of what it means to be a Sikh in America today. Despite the hardships that they endure, Sikhs continue to demonstrate their strength and resilience through their practice of the tenets of the Sikh faith, including love, service, and justice.\nA common struggle that many Sikh Americans face is the coerced decision of whether to relinquish their articles of faith in order to assimilate into Western culture and secure employment. In the San Francisco Bay Area alone, twelve percent of Sikhs have reported subjection to employment discrimination. Through the COVID-19 pandemic, employers have outwardly stated that turbans are unacceptable in the workplace, citing proper health standards as their reasoning. A video posted by Dr. Sanjeet Singh-Saluja, an emergency doctor and physician at McGill University Health Centre, went viral in 2020 as he described the “existential crisis” he faced when he was forced to shave off his beard, a symbol of the Sikh faith, in order to continue operating on patients. But in 2022, four Sikh Americans bravely filed suit against the U.S. Marine Corps, asserting their right to wear their turbans and beards and to not have to choose between their career and faith. This post addresses the heroism of these four men in asserting their legal rights under the Religious Freedom Restoration Act (“RFRA”), 42 U.S.C. § 2000bb et seq., and the First and Fifth Amendments of the U.S. Constitution in the case of Toor v. Berger.7
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".