Bibliographic record
Abstract
Almost every country now has a VAT. But is the VAT now in place in most developing and transitional countries as good as it could be? Must ‘good’ VATs always follow the same pattern? Can every country administer VAT sufficiently well to make the introduction of the tax worthwhile? Is VAT always the best way to respond to the revenue problems arising from trade liberalization? Can VAT be adapted to cope with the rising demands in some countries, especially federal countries, for more access to revenues by local and regional governments? Can VAT deal with such new problems as those arising from changes in business practices with financial innovations and digital commerce? The answers to such questions are critical in many emerging economies. VAT is too important for them not to get the answers right – or at least as right as possible. VAT remains the best form of general consumption tax available. If a developing or transitional country needs such a tax, as most of them do, then, as we suggested in Chapter 3, VAT is the one to have in almost all cases. Of course, this does not mean that the VAT most such countries already have has been either designed or implemented in the best possible way, as we discuss in Chapters 6 through 10. In addition, some serious criticisms have recently been leveled against VAT as a source of revenue for emerging economies. We consider many of these criticisms in this and the next chapter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".