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It's Cold Outside: Measuring the Challenges of Independent (Gig) Work

2020· article· en· W3046196273 on OpenAlexaff
Brianna Barker Caza, Erin Marie Reid, Susan J. Ashford, Steve Granger

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of CalgaryMcMaster University
Fundersnot available
KeywordsThrivingPsychologyDiscriminant validityStressorLonelinessSocial psychologyFace validityPsychological resilienceApplied psychologyDevelopmental psychologyClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

Gig workers face a number of challenges that differ in nature or intensity from those experienced by traditional organizational workers. We developed and tested a measure of six challenges commonly faced by independent workers—viability, organizational, identity, relational, emotional, and career-path uncertainty—in three samples of platform-based workers, including Mechanical Turk workers and highly skilled independent scientists. Initial evidence for the measure’s validity included its convergent and discriminant validity in relation to measures of depletion, thriving, resilience, and loneliness. We confirmed the measure’s factor structure, showed its relationship with thriving and perceived meaningfulness of work, and examined differences across different subgroups in the sample of independent scientists. Our findings help us to better understand the types of stressors independent workers face in the gig economy, and our diverse set of samples provides evidence of the universality of these stressors. Further, our developed measure represents an important research tool for future studies of independent (gig) workers’ experiences.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.061
GPT teacher head0.269
Teacher spread0.208 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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