Shake That Moneymaker: Insights from Montreal’s Uber Drivers
Bibliographic record
Abstract
This article presents the results of an ongoing ethnography of Uber drivers in Montreal. It draws on Jean-Pierre Durand’s “job centrifugation dynamic” (Duran, 2004) conceptual framework and offers a critique of Uber’s model of labour organization which promises “good money” and claims to create a “flexible” and “no boss” work environment. Deconstructing the Uber narrative, it exposes the central features - precarity, market control scheduling and app-subordination - which structures drivers’ daily work routines and highlights twofold process of “accumulation by dispossession”(Harvey, 2004). On the one hand, drivers’ de-proletarianization is dispossessing them from all sorts of labour protection/benefits or bargaining power. And secondly, because drivers are obliged to give the organization an unconditional access to efficiently exploit their own assets (cars/phones/Internet connection), they are being dispossessed from the value of their “dead labour” embodied in their private properties which are being monetized (Kenney and Zysman, 2016), exploited and consumed as part of the Uber process of value production.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".