Innovations in Tax Compliance : Conceptual Framework
Bibliographic record
Abstract
This paper presents a conceptual \n framework for developing more effective approaches to tax \n reform and compliance. The framework proposes that by \n combining complementary investments in enforcement, \n facilitation, and trust, reformers can not only strengthen \n enforced compliance but can also (a) encourage \n quasi-voluntary compliance, (b) generate sustainable \n political support for reform, and (c) create conditions that \n are more conducive to the construction of stronger fiscal \n contracts. A key challenge for governments lies in finding \n the right combination of these three measures -- \n enforcement, facilitation, and trust—to achieve revenue and \n broader development goals. The framework proposes greater \n reliance on locally grounded binding constraints analysis, \n coupled with careful attention to understanding politics and \n the drivers of trust in particular contexts, to guide \n analysis of how best different investments may be combined, \n prioritized, or sequenced. This framework can help policy \n makers to think about the right combination of strategies in \n specific contexts, and thus to allocate resources most effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".