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Record W2914968774 · doi:10.1017/9781108255882.024

A Critical Examination of a Third Employment Category for On-Demand Work (In Comparative Perspective)

2018· article· en· W2914968774 on OpenAlexaboutno aff
Miriam A. Cherry, Antonio Aloisi

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsIndependent contractorEntitlement (fair division)SituatedLabour lawLegal statusPerspective (graphical)Unintended consequencesBusinessWork (physics)Law and economicsActuarial scienceLabour economicsPolitical scienceLawEconomicsEngineeringComputer science

Abstract

fetched live from OpenAlex

A number of lawsuits in the United States are challenging the employment classification of workers in the platform economy. Employee status is a crucial gateway in determining entitlement to labor and employment law protections. In response to this uncertainty, some commentators have proposed an “intermediate”, “third,” or “hybrid” category, situated between the categories of “employee” and “independent contractor.”\nAfter investigating the status of platform workers in the United States, the authors provide snapshot summaries of five legal systems that have experimented with implementing a legal tool similar to an intermediate category to cover non-standard workers: Canada, Italy, Spain, Germany, and South Korea. These various legal systems have had diverse results. There has been success in some instances, and unintended consequences in others.\nAccordingly, the authors recommend proceeding with caution in considering the creation of a third category. That is due to the risk of arbitrage between the categories, and the possibility that some workers will lose rights by having their status downgraded into the third category. Cherry and Aloisi posit employee status as the default rule for most gig workers. The authors propose an exception for those working on a de minimis basis or those engaged in volunteerism for altruistic reasons.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.586
Threshold uncertainty score0.338

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 head0.035
GPT teacher head0.324
Teacher spread0.289 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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