Institutional and Political Determinants of Incentive Competition: Reassessing Causes, Outcomes, Remedies
Bibliographic record
Abstract
not effective even for the winning regions. Yet some regions do very well by attracting large, new facilities that create sustained jobs, bolster the tax base, and have multiplier effects. The nation states of Europe have developed an effective regulatory system that curtails abuse, but the United States, Canada and Australia have grappled with forms of cooperation and regulation less successfully. Incentive competition is spreading to developing countries, especially as responsibility for and fiscal capacity to support economic development has devolved to sub-national levels of government. Local governments also compete for mobile capital, export-oriented as well as retail. Incentive competition for capital is an increasingly important public policy issue, because it consumes considerable resources, alters the spatial distribution of economic activity, and entails large opportunity costs for citizens and businesses. In this chapter, we argue that incentive competition cannot be adequately approached in a game theoretic, micro-economic fashion. The phenomenon deserves an historical explanation that probes national and global institutional and political changes shaping the rise and character of bidding wars. Our treatment is thus interdisciplinary,
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".