Public Finance in the Real World: More Questions Than Answers—An Appreciation of Richard Bird's Contributions to the VAT Through Collaboration
Bibliographic record
Abstract
Richard Bird's considerable and lasting contributions to the value-added tax (VAT) field and his influence in the real world were, to a significant extent, a result of his intellectual curiosity, his discipline, and the rules of engagement in research that he followed himself and expected his collaborators to follow. The author reviews Richard's contributions to the field through the prism of their research collaborations in three areas: (1) sales taxes in Canada and other federal countries; (2) VAT in the international context and developing countries; and (3) spillovers from VAT that relate to other indirect taxes, VAT tax expenditures, and VAT gaps. Recurring themes in Richard's work are the relationship between tax policy and tax administration, and the importance of understanding the institutions that produce tax policy decisions. In the final analysis, he considered research agendas as open-ended, with more questions than answers.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".