Bibliographic record
Abstract
The base erosion and profit shifting (BEPS) project and the creation of the Inclusive Framework on BEPS are ambitious initiatives that could transform the Organisation for Economic Co-operation and Development (OECD) from a rich-countries club into the standard setter and a consensus facilitator on matters of global tax governance. In some areas, where the interests of many states have aligned, this transformation has already occurred. However, in highly disputed areas, such as the taxation of business profits in the digital economy or other distributive concerns, the OECD can only rely on shared values, which, if not entirely reconciling the conflicting interests of states, could at least encourage collective action. Since fairness underlies many common values, fairness-based narratives are a plausible source of soft power for the OECD and potentially a tool in facilitating tax cooperation. This article argues that this may be one possible explanation for the OECD's reliance on the concept of fairness in its BEPS documentation. Using the OECD's BEPS action 1 narrative as a case study and focusing on the social dimension of fairness, this article finds that this narrative is inconsistent with the states' narratives and is unjustified; it lacks fidelity (story integrity) and is only weakly persuasive, at least where the fairness argument is concerned. These flaws may not affect the OECD's legitimacy as a standard setter and consensus facilitator, but they may undermine the legitimacy of the OECD standards that are founded on fairness arguments, especially if those standards affect the distribution of the benefits and costs of tax cooperation.
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 | 0.044 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.052 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".