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Record W4361764207 · doi:10.7202/1097691ar

Exploring New Intermediaries in the Labour Market

2023· article· en· W4361764207 on OpenAlexvenueno aff
Torstein Nesheim, Kristin Jesnes

Bibliographic record

VenueRelations industrielles · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsIntermediaryWork (physics)Process (computing)BusinessKnowledge managementScheme (mathematics)Order (exchange)Service (business)PerceptionComputer scienceProcess managementIndustrial organizationMarketingEngineeringPsychology

Abstract

fetched live from OpenAlex

New labour market intermediaries, such as those using digital platforms, are challenging not only temporary help agencies but also traditional employer–employee relationships. A new conceptual scheme is proposed to distinguish between three functions: a) allocating the work; b) entering into a contract with the worker; and c) managing and organizing the work. By using this scheme in a study of 11 intermediaries of knowledge-intensive work in Norway, we found that self-service platforms are insufficient and must be supplemented with active client involvement during several stages of the allocation process. Such active involvement is driven by the complexity of the assignments and the client’s uncertainty about job requirements. Regarding management of the work, our findings contrast both with the common perception of independent contractors’ work as self-directed and with the idea that an intermediary can use algorithms to manage work. In reality, the contractor's work is managed in very different ways. Our paper outlines several approaches that combine some or all of the three functions and adds to the literature by describing new forms of triangular work arrangements.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.109
GPT teacher head0.282
Teacher spread0.173 · 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 designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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