Tax activists and the global movement for development through transparency
Bibliographic record
Abstract
Activists around the world seek to expose a global system that fails to tax multinationals adequately and thus deprives governments of needed revenues, with profound effects for development in the world's poorest nations. These tax activists have sparked a global movement, with groups all over the world seeking progress for development in poor countries by demanding greater transparency about how and how much multinational companies pay taxes. In their quest for tax transparency, the activists are inserting themselves in an elite policy-making arena that has traditionally been closed both to them and to the governments of poor countries. Their demand for a voice in global tax policy decision-making makes a claim that the individuals and leaders that are currently involved in tax policy governance cannot be counted on to concentrate on distributing the tax burden in a way that comports with broader social values. In seeking such a voice, the activists will face enormous challenges and vigorous opposition. The alternative is acquiescence to a global status quo with which fewer and fewer are satisfied, a status quo that includes severe strains on governments and growing pressure on social systems in rich as well as poor countries. In this context, tax transparency seems a plausible starting point in the quest to understand and empower the engines of economic development and prosperity throughout the world.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".