Silvia Contarini, Claire Joubert et Jean-Marc Moura (dirs), Penser la différence culturelle du colonial au mondial. Une anthologie transculturelle
Bibliographic record
Abstract
Based on the presentation of the book En attendant les robots from Antonio Casilli, this text aims to propose a reflection on the transformations of work in the digital age. Like the author of the book, we believe that behind the terms “algorithms”, “deep learning” and, above all, “artificial intelligence”, which highlight various techniques, or even “technique” understood in a more generic way, are hidden a number of social activities that can be described as “digital labor” and which largely refer to well-known processes of alienation and exploitation, characteristic of capitalism. Is there a simple reproduction of previous social relationships? Not quite. The situation could be worse and worse because the current global dynamics favor a leveling of working conditions and remuneration. So, what to do? Once again, we agree with A. Casilli about the importance of social struggles related to the world of work and when he proposes to consider work in the digital age from the viewpoint of cooperation and the principle of the “common”. Social practices that are linked to these issues can promote various forms of emancipation. Big Data should therefore be considered as resources to be shared. These issues are all the more crucial, and A. Casilli's work is so obviously relevant, given that the covid 19 pandemic has clearly given a significant boost to the digitalization of our societies and thus to digital labor.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".