Bibliographic record
Abstract
rkable 16 percentage points above the European average. s. Europe is on a corporate tax-cutting binge, with rates falling substantially since the 1990s. According to a recent survey by KPMG, the average corporate tax rate in the EU has fallen from 38 percent in 1996 to 24 percent in 2007. Data from the European Commission confirm th ing trends in the EU’s 27 member nations. The appetite for lower corporate tax rates has been sated. Further corporate rate cuts are being implemented in Germany, Estonia, Spain, and the United Kingdom, and rate cut ar Republic and France. European nations are not the only ones cutting corporate tax rates. In 2002, Australia cut its corporate tax rate to 30 percent, and now New Zealand has announced that it will cut its rate to match Australia’s. Singapore’s rate is scheduled to fall from 20 percent to 18 percent Canada is planning to drop its corporate rate by two percentage points, and Russia is considering a four entage point reduction. This shift to lower corporate tax rates is driven large by tax competition. Thanks to globalization, it is much easier for capital to cross national borders, and investors naturally prefer lower-tax jurisdictions. This is prompting g
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".