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Record W1519023994

Avoidance, Evasion, and Taxpayer Morality

2014· article· en· W1519023994 on OpenAlexaff
Allison Christians

Bibliographic record

VenueOpen Scholarship Institutional Repository (Washington University in St. Louis) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsTaxpayerMoralityEvasion (ethics)Tax evasionLaw and economicsBusinessPolitical scienceEconomicsLawPublic economicsBiology
DOInot available

Abstract

fetched live from OpenAlex

This Essay fleshes out the case for caution in employing morality as a stop-gap measure to avoid drawing a regulated line between tax evasion and tax avoidance, while still meting out punishment within the undefined space between these two poles. It suggests that the alternate view—that taxpayer behavior must be managed by law rather than social sanction—has the best chance of driving tax policy toward greater coherence in the long run. This alternate view, that tax policy must be contained in law, does not mean the public must be uninvolved in policy discourse; the opposite is clearly true. The public seems uniquely suited to the task of demanding transparency in governance as a mechanism for monitoring lawmaking and addressing tax policy problems. Transparency is of course an imperfect mechanism, but it seems to be the best hope for achieving justice across a wide variety of governance-related failures of which unjust taxation is a prominent example. Transparency forms the central core of all contemporary treatments of the problem of governance, and there is no reason why it should not also define the contours of thinking about what behaviors should be acceptable when it comes to taxation. For this reason, this Essay concludes that the problem of distinguishing tax avoidance from tax evasion presents a base case for demanding transparency in both tax information and tax lawmaking, in the service of pursuing tax justice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.240
Teacher spread0.195 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2014
Admission routes1
Has abstractyes

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