DARK SIDE CASE: Canadian Broadcasting Corporation and the Ghomeshi Sex Scandal
Bibliographic record
Abstract
This teaching case explores the problematic nature of contemporary organizations by provoking discussion on the 'dark side' issues of the Canadian Broadcast Corporation (CBC). Topics of particular importance are those pertaining to discrimination, sexual harassment, ethics, gender, age, and culture, as well as the deep structures of power that privilege certain individuals and groups at the expense of others. The focal story in the case is the sex scandal that erupted with one of CBC's most esteemed hosts, Jian Ghomeshi. On October 26, 2014, CBC severed ties with Ghomeshi after he shared with them graphic evidence involving a violent sexual encounter with a woman he had dated. After parting ways, allegations of sexual harassment, assault, abuse, and other wrongful exchanges, including charges from those who had worked with Ghomeshi at CBC, began flooding the media outlets. In the wake of the scandal, CBC had come under much criticism, particularly with respect to how the organization had handled -- or rather mishandled - - Ghomeshi during his employment. This case explores the various factors that contributed to the existence and persistence of Ghomeshi's wrongful behaviours and their effects on the various stakeholders involved, most notably the employees.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.067 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".