Shake That Moneymaker: Insights from Montreal’s Uber Drivers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it