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Record W2613155640

Written Communications and Taxpayers' Compliance: An Interactional Fairness Perspective

2016· preprint· en· W2613155640 on OpenAlexaffvenue
Jonathan Farrar, Linda Thorne

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsYork UniversityWilfrid Laurier University
Fundersnot available
KeywordsCompliance (psychology)Tone (literature)Perspective (graphical)BusinessQuality (philosophy)PsychologyAccountingPublic relationsSocial psychologyPolitical scienceComputer scienceLinguistics
DOInot available

Abstract

fetched live from OpenAlex

Written communication is the primary means used by tax authorities to communicate with taxpayers. Prior research shows that the content of written communications by tax authorities can influence taxpayers' compliance by appealing to interactional fairness. Interactional fairness refers to the quality of treatment that individuals receive from an authority figure and has two dimensions, tone and information. In written communications from a tax authority, inadvertently or by design, both tone and information are conveyed. In our study, we examine the impact of both dimensions on taxpayers' compliance through an experiment involving 287 taxpayers. We find an interaction between tone and information, such that compliance is highest in the presence of high information and an authoritative tone. We also find that compliance is positively associated with information. Our findings have practical implications for tax authorities in determining how best to use written communications to encourage taxpayers' compliance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.086
GPT teacher head0.268
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes2
Has abstractyes

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