Bibliographic record
Abstract
Subscribing to the core idea that income should be taxed where value is created, the international community has devised a set of tax base protecting rules to counter a world in which highly profitable multinational companies like Apple, Google, and Amazon pay very little in taxation. But these rules rely on assumptions about value that tend to allocate most revenues from international trade and commerce to rich countries while, whether intentionally or not, depriving poorer countries of their proper share. This Article argues that a rigorous examination of what we mean by value could prompt changes in the consensus on allocation. To demonstrate with a concrete example, the Article examines wages paid to workers in low-income countries and reveals a clear and well-documented gap between market price and fair market value resulting from labor exploitation. It then demonstrates how to apply this knowledge to existing international tax rule sets to reallocate profits to align more closely to the value-based ideal. If accepted in principle, the proposed approach could be expanded beyond wages to consider other areas in which prices do not align with value creation. Ultimately this could provide a more detailed template to reallocate multinational revenues in a way that does not inappropriately benefit richer countries at the expense of poorer ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".