A Note on the Relation between Frames, Perceptions, and Taxpayer Behavior*
Bibliographic record
Abstract
Abstract In this study, we incorporate taxpayers' threat /opportunity perceptions into our analysis of taxpayer behavior in order to refine and extend our understanding of the internal cognitive forces that shape taxpayer behavior. Decision‐making frames (that is, the gain and loss domains from the prospect theory value function) and individual perceptions (that is, perceptions of decision alternatives as being threats or opportunities) are both likely to influence behavior, yet prior research has generally ignored the behavioral effects of individual perceptions. The results of our experiment reveal that taxpayers who are due a tax refund (owe additional taxes) prior to considering a judgemental tax deduction tend to perceive the conservative (aggressive) tax deduction to be more of an opportunity/less of a threat. In turn, we find that taxpayer frames have a direct effect on taxpayer behavior and an indirect effect on behavior through their effect on taxpayers' threat/opportunity perceptions. Perhaps the most important message of this study is that researchers can advance our understanding of the internal cognitive processes that shape taxpayer behavior by incorporating taxpayer perceptions into their research designs.
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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 | 0.028 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".