Bibliographic record
Abstract
Since the COVID-19 pandemic, the global North has seen a drastic increase in usage of platform apps’ services. Platforms such as Uber, Lyft, DoorDash, and Deliveroo have become staples that connect consumers directly to workers on standby, ready to be activated at a moment’s notice to rideshare or deliver food orders. These companies offer low barriers of entry and flexible work hours, which is the ideal situation for many migrants looking for work. The scope of this research aims to build an understanding of the intersection between the rise of the far-right and the anti-immigration rhetoric in Canada and the EU that influences the hate faced by racialized migrants working highly visible gig jobs in the platform economy. By examining academic articles, news reports, and social media communications of far-right groups a major theme is apparent. The online rhetoric of the far-right can be categorized through its usage of fear and hate against racialized migrants. Migrants have been depicted as sexual threats to women, economic threats to workers, and an overall threat to the white population through demographic replacement. This conspiracy theory, known as the ‘Great Replacement’, found its way into the discursive attempts of far-right groups and political parties (ex. Chega - Portugal; VOX - Spain) to promote anti-immigration and violence against migrants. The threat that this rhetoric poses to racialized migrant workers has yet to be fully explored, and despite passing mention in a few academic articles, the violence faced by workers in the platform economy is not common in the literature, even more so in regards to its intersection with race based hate. This gap brings further questions concerning alliances among workers, advocacy groups, and trade unions based along the lines of ethnic, racial, and gendered differences to combat the growth of support for the far-right. With reasonable speculation, this research contends that far-right rhetoric and violence will continue to be targeted at racialized migrants in visible spaces and jobs, such as those in the gig economy which requires further labour solidarity and activism to aid migrants in vulnerable situations.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.058 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".