School Libraries as Panaceas for Mass Failure in West African Senior School Certificate Examinations in Nigeria
Bibliographic record
Abstract
This paper attempted to explain school libraries as reliable and dependable panaceas for mass failure in West African Examination Council Senior School Certificate Examinations (WAEC SSCE).The paper began with an introduction highlighting various comments and views on reports of mass failure in SSCE in recent years in Nigeria.This was followed by providing a brief on the concept of West African Examination Council.Percentages of students that passed SSCE from 2008 to 2012 with five credits including English Language and Mathematics were obtained from newspapers and online reports and shown in tables to confirm the claim of mass failure of students in the WAEC examinations in recent years.School libraries and their capacity to improve student performance were discussed.Some recommendations were made as regards putting in place world class school libraries in Nigerian schools and committing them to regular use by the students for success in examinations.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".