Bibliographic record
Abstract
VAT is often thought of as a relatively simple tax. Admittedly, a VAT is, by definition, simpler than an income tax for reasons of both definition (it is less ‘net’ so its base is easy to determine) and timing (there are almost no intertemporal issues in applying VAT). Nonetheless, designing and implementing a VAT are far from simple tasks. In this and the next two chapters we consider a number of design issues, leaving some important administrative questions for Chapter 9 and 10. In the present chapter, we discuss several issues in defining the base of a VAT – the treatment of real property and land, the treatment of public sector and nonprofit activities, and the treatment of financial services. These three issues have proved troublesome in practice and not easy to resolve in theory. Of course, many other design issues are also often troublesome in developing and transitional countries – for example, the treatment of agriculture and the treatment of tourism – but are not discussed in this book. Other interesting and sometimes important issues we do not discuss include the treatment of gambling, a number of issues related to VAT and services (especially cross-border services), and many aspects of VAT administration (including penalties, issues related to imports [uplifts, post-import control, etc.], and tax ‘offsets’). It would take a much longer book than this to do justice to all aspects of VAT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".