MétaCan
Menu
Back to cohort
Record W2590583113

Impact of Recession on the employment in Catalonia from a gender and age perspective

2016· preprint· en· W2590583113 on OpenAlexaboutno aff
Ma. Jesús Gómez Adillón, M. Angels Cabasés Piqué, Agnès Pardell Veà

Bibliographic record

VenueEconstor (Econstor) · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicEmployment, Labor, and Gender Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentRecessionQuarter (Canadian coin)WageDemographic economicsEconomicsInequalityAgency (philosophy)Labour economicsDistribution (mathematics)DemographyGeographySociologyEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

In Catalonia, between 2008 and 2014, the rate of youth unemployment has exponentially increased and it has turned into a structural problem: when the fourth quarter of 2014 ended, among the people under the age of 30, the number of unemployed people was 1,495,600, 645,000 more than in the first quarter of 2008. In addition, with the data provided by the Spanish Tax Agency, the average wage of wage earners over 25 years in 2014 is 3.4 times superior to the young people and the reduction of the average wage of these is 2.8 times bigger. Moreover, during this period, the annual income of women has shortened the distance in relation to men, mainly the employed group, from a ratio of 1.40 to 1.30, but in contrast, the number of women receiving minimal resources (MW and MP) has worsened: in relation to employees, 31.7% of the total number within this subgroup. In order to highlight the uneven impact of recession on the labor market in Catalonia, this study examines the evolution of its main variables in the period 2008-2014 from a gender and age perspectives delving into the structure wages and analyzing the distribution of inter-group and intra-group inequality between men and women.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.346
Teacher spread0.296 · 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 teacher head, not a consensus.

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEconstor (Econstor)Same topicEmployment, Labor, and Gender StudiesFrench-language works237,207