COMPOSING JAZZ : MELDING FOUR REGIONAL CARRIERS TOGETHER INTO AN AIR CANADA SUBSIDIARY WAS NO EASY TASK
Bibliographic record
Abstract
Air Canada Jazz is the consolidation of four airlines with disparate fleets, thousands of employees, different languages and distinct corporate cultures in a company that dates back to January 2001. Four regionals were merged into Air Canada Regional, a wholly owned subsidiary of Air Canada Enterprises. The first step was to ground the older aircraft, which reduced the fleet by 30 percent. The consolidation of the different employee groups was complicated by government restrictions on layoffs and involuntary transfers and competition requirements. In addition, the new entity had to abide by agreements to comply with the Official Languages Act, which entails preserving bilingualism among employees. Increased security costs and governmental charges for air traffic services were also a hindrance. However, unions have agreed to givebacks and concessions and have agreed to structured pay along seniority lines (for the pilots), which streamlines scheduling, since the type and size of plane is not a determinant of pay. This allows for lower training costs and relocation costs. Today, it operates some 615 daily departures to 52 destinations in Canada and 23 in the U.S. With parent Air Canada's emergence from bankruptcy, advertising and other spending to make its business grow is scheduled.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".