Disability sport profile of Ghana: evolution, policies, politics and participation barriers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".