Bibliographic record
Abstract
Introduction to the Ivey Casebook Series by Paul W. Beamish Introduction 1. Workplace Discrimination Avoiding Discrimination in Employment Selection and Retention: Some Legal Issues Staffing at Wal-Mart Stores, Inc. (A) Nextech Inc. (A) Stamford Machine Corporation: Allegations of Racism Ottawa Valley Food Products CTV Newsnet (A) 2. Sexual Harassment Sexual Harassment in the Workplace: Definition, Cases and Policy Rebecca Collier Ruth Jones (A) Telcom Most Likely to Sleep With Her Boss...and the Winner Is...Gail Wilson (A) 3. Work-Life Balance Anna Harris (A) 4. Organizational Diversity Programs Diversity: A Quota by Any Other Name? Women in Management at London Life (A) The Bank of Montreal--The Task Force on the Advancement of Women in the Bank (A) Synergy at City Hall (A) 5. Cross-Cultural Diversity Ellen Moore (A): Living and Working in Bahrain Ellen Moore (A): Living and Working in Korea Julie Dempster (A) The European Experience (A) Being Different: Exchange Student Experiences The Changing Face of Europe: A Note on Immigration and Societal Attitudes 6. Entrepreneurship Marie Bohm and The Aspect Group The Purchasing Co-Op Rubenesque Growth, Strategy and Slotting at No Pudge! Foods, Inc. English Center for Newcomers About the Editor
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.006 |
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".