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Record W2348540965 · doi:10.1080/00220388.2016.1153073

Rebuilding Local Government Finances After Conflict: Lessons from a Property Tax Reform Programme in Post-Conflict Sierra Leone

2016· article· en· W2348540965 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

VenueThe Journal of Development Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSierra leonePoliticsTransparency (behavior)UnderpinningPolitical sciencePublic administrationProperty taxLocal governmentAccountabilityEnforcementEconomic growthTax reformDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

This research interrogates the factors underpinning the relative success of a property tax reform programme in Sierra Leone. Recognising the importance of politics in shaping reform outcomes, it highlights reform strategies that have contributed to overcoming both technical and political barriers to reform. It highlights three interconnected arguments. First, there is a need for long-term, hands-on, local partnerships that support local capacity, help to confront political resistance and build a constituency for reform. Second, there should be expanded focus on politically contentious efforts to strengthen transparency, public outreach, and enforcement among elites, as they are critical to programme success and sustainability. Third, a focus on the same politically contentious elements of reform can help external actors better assess the extent of local political commitment to reform early-on, and thus target reform funding and efforts more effectively.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.057
GPT teacher head0.313
Teacher spread0.257 · 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