5. Does It Matter What Your Employees Do Outside of Work? - Off-Duty Conduct and its Implications for Organizations and their Employees
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".