Canada-United States Economic Competitiveness in a Period of Financial Instability: A Common Cause Agenda
Bibliographic record
Abstract
MR. DESJARDINS: Thank you.Good morning.Before I start, let me say a few words about Bombardier's year-end results.Yesterday, we published our year-end results for Bombardier's fiscal year, which ended on January 31, 2009.1I am happy to announce that last year was the best year ever for Bombardier.We made twenty billion in United States dollars in revenues 2 and reported a billion dollars for our net income.3 Earnings per share were fifty-six cents, 4 which is three times more than last year.We are very proud of those results; however, this positive feeling lasted only a moment, because we also announced a layoff of three thousand employees in addition to the one thousand employees that we let go a month ago, mainly within Bombardier Aerospace.6 One of the analysts this morning quoted our result as saying, "upping the train, the plane's going to be late."Let me next present what we do at Bombardier.I think it is important to understand how we manage our operation in North America and how critical an open border is to us.Our corporate offices are in Montreal.We have a Mr. Daniel Desjardins has been Senior Vice President and General Counsel at Bombardier Inc. since October 1st, 2003 and is a member of the Management Committee and of the Corporate Council of the Corporation.From
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 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".