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Record W7048261621

Las Barreras invisibles para la igualdad: tres estudios sobre las desigualdades étnicas y de género en el mercado de trabajo durante la Gran Recesión

2020· dissertation· en· W7048261621 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentQuarter (Canadian coin)RecessionSocioeconomic statusInequalityNationalityPopulation
DOInot available

Abstract

fetched live from OpenAlex

This thesis analyzes ethnics and gender inequalities in access to the labor market during the Great Recession in Spain. The first article illustrates the probabilities of unemployed people to get a job depending on gender, age, level of studies, care responsibilities and level of unemployment in the region of residence. The analytical sample is drawn from the first quarter of the Labor Force Survey for the period 2006-2016. Thanks to these data, access to employment is studied both in the moments before and after the socioeconomic crisis of 2008, taking into account the effect of fatherhood and motherhood on the labor trajectories of men and women. The results show that despite the high destruction of Inasculinized jobs, women had worse employability. In addition, it is shown that despite the fact that a higher educational level ensures better job opportunities for men and women, men ali,vays have more employability. Third, the results lend support to the hypothesis of a motherhood penalty and husband Premia in the access to the labour market. Finally, it establishes that people with foreign nationality are more likely to find a job than people with Spanish nationality. The second article of the thesis analyzes the social integration of the immigrant population and examines to what extent the changes in the economic cycle have coincided with a reconfiguration of the labor market in Spain and with a modification in the integration of foreigners. The selected data are the second quarters of the Labor Force Survey for the 2006-2016 period. The results provide support for the ethnostratification theory, showing a very unequal distribution of immigrants in the socio-occupational structure according to their origin. While immigrants from enricher countries are better located in the occupational structure, those from impoverished countries are over-represented in the lower socio-occupational classes. Although in certain cases, the post-crisis period has meant an improvement in the occupational condition of some groups, the situation is quite stable for other migrants, who seem to be stuck in a time warp. Finally, it is shown that the socio-economic recovery from 2014 only benefited the integration of people with Spanish nationality. The third and last article of the thesis analyses the transitions from the university to the labour market in Spain. Using a sample of 23,885 university graduates in 2009, I assess the probabilities of obtain a job in 2014. The results show that, even though a greater educational investment ensures better job opportunities for graduates, the probabilities of ending up over-educated are higher, especially for women. In addition, I found that graduates from health care and engineering fields of studies are more likely to obtain a job and a quality job than those from scientific, social sciences and humanities careers. Finally, it is found that people from sex-atypical field of studies (i.e. women in masculinized and men in feminized careers) are less likely to obtain a job or a quality job, especially women from masculinized fields of studies.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.020
GPT teacher head0.285
Teacher spread0.264 · 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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