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Record W4321496393 · doi:10.1002/jid.3756

Tax and governance in rural areas: The implications of inefficient tax collection

2023· article· en· W4321496393 on OpenAlex

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

VenueJournal of International Development · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of TorontoGlobal Affairs Canada
FundersDirektoratet for UtviklingssamarbeidBill and Melinda Gates Foundation
KeywordsCorporate governancePublic economicsRevenueSierra leoneEconomicsTax revenueAccountabilityFactoringRural areaDevelopment economicsBusinessFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract While there has been increasing policy attention on broadening tax bases in low‐income countries, taxing citizens in rural areas often leads to neither revenue gains nor stronger accountability outcomes. Through case studies of Sierra Leone and Togo, we demonstrate that revenue collection in rural areas is highly inefficient, leading to little, if any, revenue gains after factoring in collection costs. Accordingly, we question the existing rationales for extending taxation to rural citizens in low‐income countries. Instead, we argue for a rethinking of the role of taxation in rural areas, considering the nature of social contracts and limited fiscal reciprocity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.244
Teacher spread0.217 · 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