Bibliographic record
Abstract
Even if attention has been paid to all the elements of good VAT administration we discussed in Chapter 9, tax administration remains a difficult task even at the best of times and in the best of places – conditions seldom met in developing or transitional countries. The way a tax system is administered affects its yield, its incidence, and its efficiency. It matters. Good tax administration is both inherently country-specific and surprisingly hard to quantify in terms of both outputs and inputs. The best tax administration is not simply that which collects the most revenues; facilitating tax compliance is not simply a matter of adequately penalizing noncompliance; tax administration depends as much as or more on private as on public actions (and reactions), and there are complex interactions among various environmental factors, the specifics of substantive and procedural tax law, and the outcome of a given administrative effort (Bird 2004a). All this makes the administration of a VAT complex. We discuss in this chapter two particular issues that have proved particularly difficult to deal with in many developing and transitional countries. One issue – the refund problem – relates to keeping those within the VAT system honest; the other – dealing with the small and shadowy – relates more to ensuring that those who should be within the VAT system actually are. As a filling between these two slices of VAT administrative problems, we also discuss some general ways in which VAT administration can be improved.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".