Impact of Broken Homes on Education of Children: A Sociological Perspective
Bibliographic record
Abstract
Broken Homes, until very recently is very alien to the African family structure/setting. But it is discovered that the trend of Broken Homes is growing in the world all over and Africa is not left behind in this trend. One can deduce the growing trend of Broken Homes in Africa to the incursion of modernization and industrialization into the African family setup. The traditional African family is much knitted together with a lot of love bound. But with modernization and civilisation are fast becoming the order of the day in all sectors of daily life, family is not left behinIn Nigeria for instance, the existence of Broken Homes is unknown, and when they existed, they are ignored as exceptional cases. In Africa, no one is happy to be identified as being raised in a Broken Home. In order words, the pride of an average African Child is to be brought up in a family where the man and his dear wife are living together, loving each other and each one of them performing his/her social responsibility and obligation towards the raising of the children and the survival of the family at large. This research looked at how broken home has affected education of children in society today and proffered solutions on how the scourge could be contained in our society. The research is mainly literature and conceptual. Literature in this study was sourced mainly from secondary data like journals, books, and the views of other scholars in this field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".