We Provoked Business Students to Unionize: Using Deception to Prove an IR Point
Bibliographic record
Abstract
Abstract Many industrial relations (IR) scholars experience some angst at their (mis)placement in business schools. While our expertise broadens the curriculum, the topics central to IR and union–management matters often are met with student resistance, particularly in North America. At our wits’ end, we decided to employ a deception simulation. We devised an award winning exercise that broke business students’ psychological contract with their professor and gave them an opportunity to organize collectively to redress this injustice. Students observed first‐hand the triggers of union organizing as well as their responses to inequity. Anonymous student feedback showed an overwhelmingly positive reception to the exercise. Ethical standards developed to scrutinize deception are used to review our own exercise according to our profession’s standards. Deception is rarely used in teaching and is often associated with malevolent, callous or selfish ends. We challenge this viewpoint. Its power is in generating relevant controversies and evoking emotions that help memory consolidation.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".