Bibliographic record
Abstract
In recent years, two important new issues have been added to the list of problems facing VAT designers and administrators everywhere – the taxation of electronic commerce and the increasing interest in the possible use of VAT in some form at the subnational level of government. As we noted in Chapter 6 with respect to financial services, what most developing and transitional countries need to do is to get their VATs working properly before they begin to worry about the first of these problems. The second issue – subnational VAT – while of concern mainly to a few large countries (e.g., Brazil and India) is sufficiently important there to warrant close attention. VAT AND THE DIGITAL ECONOMY Governments, international organizations, and pundits have over the last few years poured forth reams of paper on how sales taxes should be applied to ‘digital’ sales (or ‘electronic commerce,’ hereafter ‘e-commerce’). The general line most OECD countries have taken on this issue is simple, reasonable, and persuasive: taxation should be neutral and equitable for all forms of commerce, electronic or otherwise, simultaneously minimizing both compliance and administrative costs and the potential for tax evasion and avoidance (Li 2003). But what does the growth of e-commerce imply for VAT in developing and transitional countries? VAT is a partial solution To begin with, simply adopting a VAT (compared to any other form of consumption tax) offers a partial solution to the problems posed by digital commerce.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".