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Exploring the Link between Governance and Institutions: Theoretical and Empirical Evidence from Tanzania

2015· article· en· W2188100487 on OpenAlexaff
Boniface E. S. Mgonja, Alphonce W Dossa

Bibliographic record

VenueHumanities and Social Sciences Letters · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsUniversité du QuébecUniversity of Alberta
Fundersnot available
KeywordsCorporate governanceTanzaniaPoliticsProject governanceMulti-level governanceEmpirical evidencePolitical scienceEmpirical researchGood governanceInstitutionalismEconomic systemPublic administrationSociologyEconomicsManagementSocioeconomicsLaw

Abstract

fetched live from OpenAlex

The potential link between governance and institutions is increasingly becoming a central concern in social science. In political science, the approach taken to explore this link involves examining the role structure plays in determining political behaviours, the overall patterns of governance, and the outcomes of political processes. Therefore, the quality of institutions has long been recognized as an important component of a well-functioning system of governance. This paper investigates and reflects on the relationship between institutions and governance in local political settings and analyzes the impacts of institutional factors on good governance. Very specifically, the paper explores different theoretical and empirical debates about governance in general and good governance in particular. Drawing upon “historical institutionalism”, the paper offers a satisfactory analytical framework for studying the ability of the institutions of governance in Tanzania, from their inception through their development over time, to meet the needs of the local community.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.488
GPT teacher head0.363
Teacher spread0.125 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2015
Admission routes1
Has abstractyes

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