Ryerson, Toronto ANOTHER ONE BITES THE DUST: TURNOVER AND FAILURE AMONG THE LEADING CANADIAN FIRMS, 1973-2003
Bibliographic record
Abstract
The failure of many of the largest domestically-controlled corporations to survive is examined by reviewing the scale of turnover from 1973 to 2003. To account for the degree of change, the analysis configures the defunct enterprises into six parables of perdition. The final quarter of the twentieth-century was characterized by contradictory trends in Canadian business. Despite cyclical changes, the economy grew at a stronger pace than was the case for most of the industrialized nations. In contrast, productivity rates lagged behind the levels achieved in western Europe or the United States. Numerous Canadian firms, which traditionally had focussed upon the domestic economy, evolved into multinational enterprises and, in a few cases, transplanted their headquarters abroad. Concurrently, with lower trade barriers and the transition to NAFTA, foreign firms penetrated activities dominated by domestic companies. Trade and investment patterns became continental in scope, while mergers and acquisitions swelled the size of major corporations. Analysts heralded the transition to a ‘new economy ’ dominated by service and technologically-advanced enterprises, even as a growing proportion of the biggest Canadian firms were located in resource or trailing-edge sectors. Large corporations, as Chandler (1977, 1990) has noted, were key in the construction of
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".