Novo Capitalismo e uberização do trabalho: um estudo sobre a subjetividade do trabalhador motorista de Uber
Bibliographic record
Abstract
This research seeks to understand the phenomenon of uberization of work in its macro-structural and global characteristics, considering mainly the moral perspectives of understanding contemporary society, without failing to emphasize the differences in the issue of work in the center and periphery of capitalism. I specify Uber drivers as an object of analysis, but place it in a larger context of uberization. First, I seek to understand how uberization is situated in a global context of precariousness based on theoretical and empirical studies by authors such as Richard Sennett, Dardot and Laval (2016) and Robert Castel (1997;1998). The objective of this first stage is to assess the phenomenon of uberization of work and establish a relationship between transformations of capitalism and the world of work, as well as to examine trends in uberization and identify the ways in which digital platforms, in particular, Uber, act. Afterwards, I seek to analyze the empirical experience of drivers from the qualitative method of in-depth interviews, inspired mainly by the work of Jessé Souza in the Ralé brasileira (2018) using the concepts of unworthy work (MACIEL 2007) and neoliberal subject (LAVAL DARDOT, 2016)) as an analytical key to understand the subjective and psychosocial aspects present in the life trajectories and work of Uber's driver. Finally, preliminary results demonstrate congruence with the chosen theoretical approach, especially with regard to the debate of neoliberal subjectivity.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".