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Record W3091860498

Rotación ocupacional y calidad del empleo

2020· article· es· W3091860498 on OpenAlexaboutno aff
Roxana Maurizio

Bibliographic record

VenueDesarrollo Económico · 2020
Typearticle
Languagees
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansQuarter (Canadian coin)WageDemographic economicsWelfare economicsGeographyEconomicsLabour economicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Self-employment continues to be a very relevant type of labor insertion in Latin America where, at least, a quarter of occupied people are non-salaried workers. From a dynamic point of view, in turn, this group of workers exhibits patterns of labor turnover significantly different from those experienced by wage earners. This is particularly important in a region characterized by very marked economic cycles and scarce coverage of social protection. This paper analyzes the occupational mobility of independent workers in six countries of the region -Argentina, Brazil, Ecuador, Mexico, Paraguay and Peru-, between 2002 and 2015. In particular, it estimates the intensity of the transits of these workers in comparison with employees, analyzes the characteristics of these transitions and identifies the factors associated with them. This evidence allows us to evaluate the validity in the case of Latin America of the hypotheses usually used to analyze the behavior of other labor markets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.002

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.

Opus teacher head0.026
GPT teacher head0.275
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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