Bibliographic record
Abstract
Plant deaths arise from failure when firms exit an industry. Plant deaths are also associated with renewal when incumbent firms close down plants and modernize their production facilities and start-up new plants. The rate of plant deaths affects the amount of change that occurs in labour and capital markets. Plant deaths result in job losses and incur significant human costs as employees are forced to seek other work. The death process also gives rise to capital losses - to the loss of earlier investments that the industrial system had made in productive capacity. This paper makes use of the plant-death date to provide new information on the likely length of life of capital invested in plants. This paper measures the death rate over a forty year period for new plants in the Canadian manufacturing sector. It develops a profile of the death rate for entrants as they age. On average, 14% of new plants die in their first year. Over half of new plants die by the age of six. By the age of 15, less than 20% are still alive. As a result, manufacturing plants have relatively short lives. The average new plant lives only nine years (17 years if the average is employment-weighted). These rates vary by industry. The longest length of life (13 years) can be found in two industries -primary metals and paper and allied products. The shortest average length of life (less than 8 years) occurs in wood industries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".