MétaCan
Menu
Back to cohort
Record W4251713102 · doi:10.5539/jsd.v7n5

Human Resource Capacity Needs at the District Assemblies: A Study at Assin South District Assembly in Ghana

2014· article· en· W4251713102 on OpenAlexvenueno aff
Daniel Odoom, Christian Kyeremeh, Ernest Opoku

Bibliographic record

VenueJournal of Sustainable Development · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsRemunerationBusinessNonprobability samplingIncentiveGovernment (linguistics)Capacity buildingDecentralizationHuman resourcesAutonomyResource (disambiguation)ScheduleOperations managementMarketingEconomic growthFinanceManagementEconomicsMedicinePolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

The study examined the human resource (HR) capacity needs at the Assin South District Assembly using the descriptive and non-interventional research design. Purposive and quota sampling techniques were used to select 53 respondents with interview schedule and interview guide as the research instruments. The study revealed that the existing institutional arrangements in Ghana’s decentralized government system did not allow the Assembly to properly address its HR capacity needs. Besides, poor staff attraction, low remuneration, poor accommodation facilities and inadequate training and development contributed to the Assembly’s HR capacity needs. The study recommended that the government should decentralize the HR arrangements at the DAs to grant autonomy to the Personnel Management Department at the Assembly to actively address its own HR capacity needs. The government must effectively collaborate with the Assembly to provide adequate logistics, attractive incentives and social amenities so as to attract well-qualified personnel for smooth decentralization.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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.

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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicLocal Government Finance and DecentralizationFrench-language works237,207