“Hey Cabbie! Where are you From?” An Examination of Everyday Racism in Toronto’s Taxi Industry
Bibliographic record
Abstract
Using an anti-racist Marxist lens, issues of social exclusion and settlement are broadly highlighted taking into account racism in an industry that is most commonly noted for its ease of entry for immigrant professionals. This study attempts to build on previous studies of Toronto’s taxi industry (Hathiyani, 2006; Abraham, Sundar, & Whitmore, 2008) to focus specifically on racism. This research paper examines the extent to which ‘everyday racism’ is both a by-product of and a critical ingredient in perpetuating structural racism, using Toronto’s taxi industry as a case study. Drawing on interviews from 18 fulltime taxi drivers who identified as racialized groups and were born outside of Canada, it describes the familiar tensions associated with experiencing and responding to instances of racism in a precarious industry. In the absence of an association, anti-discrimination or workplace rights to protect the driver against racial abuse and harassment, drivers are forced to negotiate their responses on an individualized basis. Drivers linked everyday racism to both class position and structural racism within the industry. These findings strongly demonstrated inadequate policies to protect drivers from everyday racism in the workplace as a result of both structural racism and a neo-liberal climate. This warrants further inquiry as Toronto’s taxi industry is a major employer of racialized, immigrant men.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".