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Record W4387306378 · doi:10.1177/00221856231204486

Employment in a post-colonial society – The case of Greenland

2023· article· en· W4387306378 on OpenAlexaboutno aff
Rasmus Lind Ravn, Laust Høgedahl

Bibliographic record

VenueJournal of Industrial Relations · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRespondentIndigenousDanishEducational attainmentDemographic economicsPopulationLogistic regressionDemographyColonialismEthnic groupEconomicsGeographyPolitical scienceSociologyEconomic growthMedicine

Abstract

fetched live from OpenAlex

In the fields of labour market research and industrial relations research, there is increasing interest in post-colonial societies and the labour market outcomes of indigenous peoples. However, existing research has generally underexplored the Greenlandic labour market. This is particularly true for factors associated with the Greenlandic Inuit population's employment outcomes. In this article, we investigate barriers and potentials for labour market participation in Greenland, focusing on individual-level factors that promote or inhibit the likelihood of being employed. We use a unique, nationally representative survey of the working-age population and explore these factors through a series of logistic regression analyses. We find that educational attainment, positive self-assessed health, and the number of people in the household were positively related to employment. Our most important findings and contributions are that respondents who answered the survey in Greenlandic were less likely to be employed compared to those who answered it in Danish. Furthermore, if a respondent was born in Greenland, compared to being born in Denmark, it lowers the likelihood of being employed. We interpret this disparity as evidence of an ethnically segregated labour market with indications of discrimination.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.343
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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