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Americans with Disabilities Act

2003· article· en· W1983130440 on OpenAlexaff
Nathan Odem, Peter Blanck

Bibliographic record

VenueSpine · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsStatuteMedicineLiabilityGeneral partnershipCorporationNinthShareholderLimited liability partnershipPublic relationsLawBusinessLiability insuranceFinancePolitical scienceCorporate governance

Abstract

fetched live from OpenAlex

This article examines the application of Americans with Disabilities Act requirements to professional associations like physician practice groups. In general, employers with 15 or more full-time employees must comply with the Act. However, the definition of an employee is sometimes unclear, especially as applied to business entities commonly used by physician practice groups. A recent case decided by the United States Court of Appeals for the Ninth Circuit held that physician-shareholders of a professional corporation are employees for Americans with Disabilities Act coverage purposes. Analogous cases in other federal circuits have held differently, likening the "owners" of professional corporations to partners in a partnership, who are not considered employees. Similar questions arise for popular business entities, such as Limited Liability Companies and Limited Liability Partnerships. This article discusses the nature of the business forms commonly used by physician practice groups and how their characteristics impact employee status for Americans with Disabilities Act coverage. It then suggests that examination is useful beyond business formation characteristics to the purpose of the Americans with Disabilities Act and other employment antidiscrimination statutes.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0130.007

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 head0.032
GPT teacher head0.247
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2003
Admission routes1
Has abstractyes

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