Policy Forum: Transparency—An Essential Condition for Ethical Behaviour in Tax Planning
Bibliographic record
Abstract
Ethics play a significant role in a critical assessment of tax practice, but ethical standards on their own have a rather limited influence on tax-planning decisions; therefore, transparency is a necessary tool. Tax planning has traditionally been driven by cost-benefit analysis and by a risk-management perspective. However, closer public scrutiny of corporate tax affairs and increased disclosure requirements have put pressure on tax-planning decisions, thus adding an ethical dimension to the risk-management perspective. Transparency is certainly essential in limiting the aggressiveness of tax planning, but its impact on ethics may be broader in providing an occasion to change the "moral meanings" in relation to tax-planning activities for those involved.
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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 | 0.047 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.085 | 0.032 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".