Bibliographic record
Abstract
Preserving cultural heritage, mainly through Indigenous play, is imperative for fostering the younger generation's comprehension of diverse cultural aspects. In the 21st century, shifts in the educational landscape have jeopardised early-grade learners' involvement in Indigenous play in Nigeria, jeopardising the nation's rich multicultural diversity. This research investigates the pedagogic significance of Indigenous play for early-grade learners in the Owerri education zone of Imo State, Nigeria. The study was conducted in the Owerri education zone of Imo State, Nigeria. The setting includes schools, homes, and communities where young learners traditionally engage in Indigenous play. Utilising qualitative methods and a narrative research design, the study explores factors contributing to the decline of Indigenous play through interviews, observations, and literature reviews. The findings reveal decreased Indigenous play, highlighting various game types and benefits crucial for children's physical, socio-emotional, and academic development. Safety concerns, parental emphasis on intellectual development, and social media and technology influence diminish opportunities for early-grade learners to engage in Indigenous play. The research underscores the critical role of Indigenous play, emphasising the adverse effects of its decline and advocating for a balanced education approach prioritising cognitive and affective development.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".