Bibliographic record
Abstract
Why this book? In the aftermath of a global financial crisis, the past decade has been characterized by increased fiscal pressure. Against this backdrop, it is no surprise that many have called for strengthened efforts in domestic resource mobilization. Domestic resources are the largest untapped source of development financing, but the effective mobilization thereof poses significant challenges in terms of revenue policy and administration strategies. This book provides an overview of various policies that can significantly contribute to increasing domestic revenues by enhancing tax compliance, curbing tax evasion and improving the relationship between taxpayers and tax administrations. It consists of national reports from 33 countries around the globe, initially discussed at the conference “Improving Tax Compliance in a Globalized World” in Rust (Austria) from 30 June to 2 July 2016. The book explores various approaches to improving tax compliance. Access to tax information is at the centre of the debate, including the collection of third-party information, information obtained as a result of cooperation between tax administrations and as a result of inter-agency cooperation between tax administrations, financial intelligence units and law enforcement agencies. Moreover, the book sheds some light on alternatives to improve tax collection and suggests potential measures to enhance voluntary tax compliance. Finally, it looks forward to potential challenges that may be faced by tax administrations in the future. Taken as a whole, this study contributes to the challenging task of making tax administrations more effective and more efficient. Downloads Sample excerpt, including table of contents This book is part of the WU Institute for Austrian and International Tax Law – Tax Law and Policy Series. View other titles in the series
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.477 | 0.236 |
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".