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Record W2765609143 · doi:10.1111/issr.12141

<b>Does trust increase willingness to pay higher taxes to help the needy?</b>

2017· article· en· W2765609143 on OpenAlex
Nazim Habibov, Alex Cheung, Alena Auchynnikava

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Social Security Review · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRedistribution (election)SolidarityInterpersonal communicationWelfarePublic economicsWelfare stateSocial trustState (computer science)Willingness to payBusinessEconomicsDemographic economicsSocial psychologyPolitical scienceMicroeconomicsPsychologyMarket economy

Abstract

fetched live from OpenAlex

Abstract The article studies the causal effect of trust on the willingness to pay higher taxes to help the needy in a sample of 29 countries of Eastern and Southern Europe, and the former Soviet Union and Mongolia. It is hypothesized that interpersonal trust leads to a greater willingness to pay taxes to help the needy since (i) trust increases the likelihood of helping strangers; (ii) trust fosters solidarity and cooperation when working to solve common problems in society; and (iii) trust reduces suspicion with respect to the perceived misuse of redistributed money. Three key findings are that the more people trust each other, the more they are ready to support the welfare state; the effect of trust on welfare state support holds even in a contextual environment characterized by rather lower levels of trust and relatively underdeveloped systems of redistribution; and higher individual‐level trust fosters tax morale and helps deter tax evasion.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.049
GPT teacher head0.308
Teacher spread0.258 · 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