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Record W1998992462 · doi:10.1177/0042098011431615

Negotiating Networks of Self-employed Work

2012· article· en· W1998992462 on OpenAlexfundaboutno aff
Tara Fenwick

Bibliographic record

VenueUrban Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoUniversity of Alberta
KeywordsNegotiationSociologyWork (physics)Ethnic groupGender studiesBusinessEngineeringSocial science

Abstract

fetched live from OpenAlex

Within the increased flexible, contracted work in cities, employment is negotiated through network arrangements characterised by multiplicity, mobility and fluidity. For Black and minority ethnic group members, this network labour becomes fraught as they negotiate both their own communities, which can be complex systems of conflicting networks, as well as non-BME networks which can be exclusionary. This discussion explores the networking experiences of BME individuals who are self-employed in portfolio work arrangements in Canada. The analysis draws from a theoretical frame of ‘racialisation’ to examine the social processes of continually constructing and positioning the Other as well as the self through representations in these networks. These positions and concomitant identities enroll BME workers in particular modes of social production, which order their roles and movement in the changing dynamics of material production in networked employment.

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)0.007
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.021
Scholarly communication0.0110.008
Open science0.0020.013
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.051
GPT teacher head0.314
Teacher spread0.263 · 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

Citations6
Published2012
Admission routes2
Has abstractyes

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