Taxis vs. Uber: Courts, Markets, and Technology in Buenos Aires, by Juan Manuel del Nido
Bibliographic record
Abstract
acques Rancière views politics as an arrangement to deal with differences within a common place (Rancière 2000).Therefore, politics is not a place of agreements.On the contrary, a healthy political situation is one in which people debate, show discomfort, and refute each other.In this "distribution of sensible," Rancière argues that there are relations between shared sceneries and the division of exclusive parts.This distribution includes how people engage with legislation, institutions, other people, and situations they may face.Politics is thus a social experience.However, there are situations where political engagement does not occur, even as public debate rages.In Taxis vs. Uber, Juan del Nido discusses how the population of the autonomous city of Buenos Aires engaged in post-political reasoning when a debate about Uber began in the Argentinean capital in 2016.The book draws on ethnographic fieldwork in Buenos Aires between July 2015 and August 2016 and again in 2017.While most of the people this ethnography is about are middle class, it is not a study of the middle class so much as a study of the kind of thinking del Nido encountered among people who fit a certain socioeconomic definition of the middle class.The political context presented in Taxi vs. Uber concerns the arrival of Uber-a global multinational corporation that offers shared rides-to Buenos Aires, where taxi drivers had an "alleged" monopoly on transportation in the city.Against this backdrop, del Nido discusses how a myriad of moralities (such as choice, freedom, and competition) are mobilized by the subjects interested in these issues.Ultimately, this debate could be reduced to a simple question: What do people want?Taxis or Uber?However, reducing a complex discussion
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".