Politics, Disability Governance and Inclusiveness of Parasport Athletes in a Coronavirus Pandemic Aftermath in Africa: Observations from Nigeria
Bibliographic record
Abstract
The paper evaluates politics and governance underlining disability inclusion development using reflections in parasport. Its thesis-of-thesis derives from the presentation of surveyed explanations from Nigerian stakeholders in the Paralympics sector to generalize for Africa. Before the COVID-19 pandemic outbreak, politics in Africa shows an abysmal scorecard in terms of combating discrimination against disabled persons. Accordingly, the continent's disability inclusiveness governance shows it is effectual. Thus, there is perhaps ample indication to adduce that sports politics will continually fail to achieve the inclusiveness of parasports athletes in a coronavirus epidemic aftermath. Using the Nigerian context, the paper gathers evidence from interviews with stakeholders and evaluative-secondary data in parasports concerning not only to responding to disability inclusion in sports but also to the wider politics of sustaining inclusiveness of Paralympic athletes in a post-COVID-19 era. The paper argues that the character of politics in Africa generally has not resulted in optimal investments, considerations, and willpower from political leaders to advance outcomes in the aspect of inclusivity of athletes with disabilities. It considers contextual factors that militate against achieving all-inclusiveness of disabled sports persons and how politics can be channelled to achieve their optimum well-being in the sports arena.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".