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Record W1582473911 · doi:10.26522/ssj.v1i2.974

The Knowledge Economy, Gender and Stratified Migrations

2007· article· en· W1582473911 on OpenAlexvenueno aff
Eléonore Kofman

Bibliographic record

VenueStudies in Social Justice · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipSubject (documents)GeopoliticsPromotion (chess)InequalitySociologySocial exclusionContext (archaeology)Social citizenshipPolitical sciencePolitical economyEconomic growthEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

The promotion of knowledge economies and societies, equated with the mobile subject as bearer of technological, managerial and cosmopolitan competences, on the one hand, and insecurities about social order and national identities, on the other, have in the past few years led to increasing polarisation between skilled migrants and those deemed to lack useful skills. The former are considered to be bearers of human capital and have the capacity to assimilate seamlessly and are therefore worthy of citizenship; the latter are likely to pose problems of assimilation and dependency due to their economic and cultural ‘otherness’ and offered a transient status and partial citizenship by receiving states. In the European context this trend has been reinforced by the redrawing of European geopolitical space creating new boundaries of exclusion and social justice. The emphasis on the knowledge economy also generates gender inequalities and stratifications based on skills and types of knowledge with implications for citizenship and social justice.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.083
GPT teacher head0.419
Teacher spread0.336 · 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 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

Citations46
Published2007
Admission routes1
Has abstractyes

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