Bibliographic record
Abstract
There are two very different views about the value of subsidizing otherwise unviable businesses in declining regions of the economy.One view emphasizes that an economy cannot succeed by investing in money-losing businesses.Kristian Palda, professor emeritus in the faculty of business at Queen's University, recently wrote of the Export Development Corporation of Canada: "The logic is that by bleeding healthy parts of the economy to coddle non-performers, Canada will breed efficient businesses, or businesses that serve some vaguely defined national interest" (Palda, 2000).The second view was exemplified by the Premier of Quebec, Lucien Bouchard, when he spoke in 1998 of the "boost" that the economy of Quebec had received from spending to repair the ravages of the great ice storm.The repairs made the provincial gross domestic product figure look good for a year or two.The "impact" was a higher "growth" rate.GDP numbers are key, even if the activity is debt-supported and profits and wealth have taken a beating.The article "Evaluating Policy Outcomes: Federal Economic Development Programs in Atlantic Canada" is an extreme version of the second view.Not only does it claim that it is costless for the federal government to tax the prosperous areas of Canada to provide subsidies to business in depressed areas, it is actually profitable.The authors state that, in 1997, the federal government reaped approximately five times as much in tax revenues "resulting from ACOA expenditures" as it made in grants.Over the period of the study, manufacturing employment in the Atlantic provinces dropped 18.1%, and business employment overall fell by 6.1% (figures 5 and 6).What is the explanation for the disparity between the rosy view of a great success of subsidies in Atlantic businesses and the disastrous economic bottom line?The authors argue that the non-subsidized part of the economy performed
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.023 | 0.055 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".