Dark Side Case: The Derailment - A Role-Playing Case of On- and Off- Duty Conduct(or)
Bibliographic record
Abstract
Stephanie Katelnikoff was conductor of a CP Rail freight train loaded with lentils and fly ash when it derailed. Less than one month later, she was terminated for breach of policy on reporting injuries related to the derailment and for talking to a reporter about the derailment. She was reinstated with back pay by an arbitrator only to be terminated again less than two years later. Her employer argued the dismissal was based on comments made about CP and photos on company property, both posted to social media. The case – which is disguised – and associated notes consider employment relations, human resource management, and gender issues in managing performance, misconduct, and discipline in a male-dominated workplace. The case can be used as a problem and decision-oriented role-playing exercise, a pair of linked decisions in which students must defend what they would do: dismiss, discipline, or nothing at all. The case can also be used to examine issues such as discrimination and gender from an evaluative perspective. It examines the workplace critically, as a site of inequality and gender discrimination, as well as of oppression and harassment. Students should find this case challenging in that making decisions and recommendations require them to balance the employer’s prerogative to determine the composition of its workforce with the creation of gender-inclusive workplaces and worker rights. This case is suitable for undergraduate, graduate, and executive education in employment relations, human resource management, business ethics, gender and diversity studies, and organizational behaviour.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".