Transcending dichotomies: Informal work, young people and the state in Argentina
Bibliographic record
Abstract
Since the beginning of modern labor markets, formal and informal activities have coexisted alongside each other. However, most of the traditional approaches to informality are not fully able to explain the multiple forms by which this phenomenon manifests itself today in certain contexts. Informality in Latin American societies, particularly in Argentina, is heuristically revelatory for illustrating the need for a more complex definition and less rigid theoretical classifications. The main purpose of this article is to contribute to an empirical analysis about informality by addressing two points. First, the article provides a better understanding of the heterogeneous nature of informality—“involuntary” or “voluntary” informal work, “half-formal/half-informal” work—in the field of youth employment. Second, in connection with debates about the sources of informality, the article explores the hypothesis of the existence of state-created informality. The article illustrates these points at different levels: youth careers, employers’ recruitment strategies, and states policies. This perspective seems useful for identifying the actors involved, acknowledging the blurry boundaries between diverse informal situations, and understanding actors’ uses of them. The points are illustrated by evidence from a qualitative and longitudinal study on youth employment careers in Argentina. The article concludes with a discussion about some key issues of informality, such as legality, regulation and agency.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".