A tax by any other name? Conceptions of taxation and implications for research
Bibliographic record
Abstract
Abstract As taxation has become a prominent issue on the international development policy agenda, a growing body of research has focused on taxpayer perceptions and experiences of taxation. A strand of this research emphasises the importance of the historical, political and social context of taxation. We position ourselves in line with this research as we pay attention to the emic definitions of taxation in Africa across contexts, languages, and time periods. We explore how the conception of taxation in different contexts is closely interrelated with the language used to describe it, with language being a product of histories of colonialism, conflict, and extraction by social, traditional and political actors. We argue that studies of taxation, particularly survey-based research, need to be complemented, if not informed, by a deeper understanding of the diversity of tax landscapes and of the meanings ascribed to taxation in a given context. This will strengthen content and interpretive validity of taxpayer perception data as well as provide important nuances to the understanding of the dynamics of taxpayers’ experiences of contemporary states and systems of taxation.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".