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Record W3157089106 · doi:10.24908/iqurcp.9078

5. Does It Matter What Your Employees Do Outside of Work? - Off-Duty Conduct and its Implications for Organizations and their Employees

2016· article· en· W3157089106 on OpenAlexvenueaboutno aff
Matei Olaru

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsReputationDutyArbitrationArgument (complex analysis)Sample (material)Work (physics)BusinessPoliticsPublic relationsLegislationPolitical scienceLawCriminologyPsychologyEngineering

Abstract

fetched live from OpenAlex

This research is among the first to explore the phenomenon of off-duty conduct and its implications for organizations and their employees. This research reviewed cases of offdutyconduct to understand the nature of conduct that faced arbitration, the discipline imposed for the conduct, and the arguments made by the parties about the impact of theconduct on the organization itself. In 1967, a case in Ontario (Re Millhaven Fibres Ltd. & Oil, Chemical and Atomic Workers I.U. Loc. 9-670,[[1967] O.L.A.A. No. 4]) set out five factors to evaluate the impact of an employee’s off-duty conduct on the organization. Analysis was based on explicit mention of one or more Millhaven principles. The research included 116 diverse Canadian arbitration cases. Examples of infractions in the sample pool include: theft, drug trafficking, vandalism, assault, tax fraud, murder, sexual assault, drug manufacturing, impaired driving, political protest, and defamatory statements. Trend analyses led to the preliminary conclusion that employers will use reputational damage (one of the five Millhaven principles) as an umbrella defense in arbitration. These findings raise the question of the effectiveness of the reputation Millhaven principle as a valid argument. The initial findings from this research also shed some light on organizations’ reactions to the off-duty conduct of their employees. Further research of interest will include a larger sample with reputation specific grievances to determine if an abnormally large number of reputation-based grievances are allowed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0000.001
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 head0.154
GPT teacher head0.363
Teacher spread0.209 · 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.

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
Published2016
Admission routes2
Has abstractyes

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