The Counting Machinery: Translation, Multiplication, and Liberal Politics of Homelessness in Paris
Bibliographic record
Abstract
Abstract This article analyzes the interconnected translation processes that led the Paris city council to conceptualize, address, and act upon “homelessness” through counting. By translation, we mean a range of semiotic processes that connect social worlds, their objects, practices, genres, and bodies of expertise. These are usually imagined as separate: For example, auditing and volunteering, science and government, charity and policing, poverty and social hygiene. Our analysis is based on ethnographic data collected in Paris, France, between January and August 2023, during two editions of the Nuit de la Solidarité [Night of Solidarity], a large-scale effort by the city council, in collaboration with numerous volunteers, to count homeless people in Paris. Linking translation scholarship with academic work on quantification and liberal governmentality, we demonstrate that the semiotic process of translation is deeply interconnected with the political work performed by numbers and counting techniques, imbuing them with meaning and ensuring their capacity to exert power. Translation, we show, serves not only to link governance techniques across geopolitical borders but also to integrate various political projects and normalize and naturalize the structural inequalities that define cities like Paris.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".