Bibliographic record
Abstract
VAT is as VAT does. The way VAT is administered determines its effects. A full discussion of all aspects of VAT administration would require a separate book. All we can do in this and the next chapter is to highlight a few issues that experience suggests are important in developing and transitional countries. In Chapter 3 we asked whether every country needed a VAT. We almost – though not quite – answered yes to this question. It thus seems appropriate to begin the discussion of VAT administration by saying a few words about the way a country that previously has not had a VAT should launch one. LAUNCHING VAT Experts tell us that a preparatory period of between 18 and 24 months is necessary to set up a VAT (Tait 1988). Experience confirms that this advice is reasonable. Some countries that have tried to move to a VAT more quickly have paid a substantial price for their haste and have found it difficult subsequently to get it right. On the other hand, experience also suggests that too long a preparation period may sometimes be costly. Since the window of opportunity to introduce major tax changes may be open only for a short time, countries adopting a VAT must sometimes take what may be called the ‘big bang’ approach. VATs introduced too quickly have not always worked out well, of course, and that is why experts so often emphasize the desirability of following the normal schedule mentioned.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.020 |
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".