Bibliographic record
Abstract
The 2012 transatlantic corporate tax scandals surrounding Amazon, Google and Starbucks exposed the scope and gravity of offshoring by digital multinationals, producing major dilemmas for the global governance of international fiscal law and policy. The Organisation for Economic Co-operation and Development [OECD, 2015] estimates corporate profit shifting erodes up to $240 billion dollars from the global tax base every year. Extant literature has established links between corporate tax avoidance and accelerating domestic socio-economic inequality, the declining redistributive capacity of states, the malign impact on growth of gross domestic product (GDP), and the rise of antiglobalism in the form of populist discontent, providing unambiguous evidence that global tax competition causes social and economic harms. Responding to public outcry, world leaders at the Los Cabos summit in 2012 tasked the OECD with tackling national tax base erosion, and the Inclusive Framework for the Base Erosion and Profit Shifting (BEPS) initiative currently boasts 123 participating states, including 93% of global GDP. Although taxation is at the heart of the social contract, this new global tax governance is both under-theorized and poorly understood in the mainstream political science literature. This study draws on the extra-disciplinary approach of law and economics to argue that while global tax cooperation under Group of 20 (G20) -OECD auspices may be considered a success ex ante based on the record of first-order compliance, the ex post enforcement dimension of BEPS is both under-theorized and under-scrutinized empirically. This study contributes a novel meta-theory of the new global tax governance that accounts for the variables of time, institutional sphere of action and policy feedback loops embedded in the global fiscal policymaking process to stimulate further inquiry. It concludes with recommendations for global fiscal policy, further research on BEPS and G20 digital governance.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".