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Record W4413543790 · doi:10.3389/fspor.2025.1645536

Disability sport profile of Ghana: evolution, policies, politics and participation barriers

2025· review· en· W4413543790 on OpenAlexaff
Derrick Charway, Dennis Osei-Nimo Annor, Davies Banda

Bibliographic record

VenueFrontiers in Sports and Active Living · 2025
Typereview
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsWestern University
Fundersnot available
KeywordsPoliticsPolitical scienceDevelopment economicsEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

The profile analyses the landscape of disability sport in Ghana, tracing its historical evolution and contemporary challenges. Alongside legislative advancements and the dedication of various stakeholders, an increase in the persons with disability population has been observed. Based on data from the Ghana Statistical Service census, this demographic rose from 737,743 in 2010 to 2,098,138 in 2021, constituting 3% and 8% of the Ghanaian population in those respective years. Nevertheless, significant barriers to the mainstreaming of disability sport persist. The analysis delves into the interplay of cultural norms, government policies, and collaborative efforts in shaping the trajectory of disability sport in the country. Insights into the population of persons with disabilities and their engagement in sport offer a foundation for discussion. Further, an analysis of the roles of key state and non-state organisations, alongside international partners, emphasises the need to move from symbolic implementation to genuinely inclusive implementation. Contemporary issues such as political infighting, inadequate funding, gender dynamics, limited media coverage, exploitation of disability sport and systemic neglect continue to hinder progress. The profile underscores the urgent need for sustained policy implementation, increased investment, and a more inclusive and collaborative approach to secure a promising future for disability sport in Ghana.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.578
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.339
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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