Bibliographic record
Abstract
New taxes aiming at tackling the challenges of this age are popular. Leading principles are Always somewhere and The Polluter has to pay. In order to tackle the challenges of Base Erosion Profit Shifting: Base Erosion Profit Shifting (diverted profit taxes in Australia (as from 1 July 2017) and the UK); the digital economy (an equalization levy (suggested by the OECD) in India); and climate change (Carbon taxes in Finland, Norway, Sweden, Denmark, Iceland, Switzerland, Latvia, the UK, Portugal, Costa Rica, Japan, South Africa, Chili, Britis Colombia, City of Boulder, Colorado, San Francisco Bay Area, Montgomery County, Maryland and California, France (as of 2017) and most likely Canada (as of 2018). The Netherlands at the time of writing has no plans to introduce similar taxes. The new taxes disturb the level playing field (risk of double taxation), complicate the tax system and are not transparent as the legislation is generally not accessible at all and if so most times not in English on the Internet. Little information is available on the economic and juridical effects of the diverted profit tax and equalization taxes. Research on the economic effects of carbon taxes is available, but little attention has been paid thus far to the juridical aspects of these taxes. The author argues international organizations should provide more guidance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.287 | 0.147 |
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".