Managing Higher Education Needs of Internally Displaced Persons in Cross River State, Nigeria
Bibliographic record
Abstract
Displaced persons encounter some difficulties in accessing higher education, yet higher education remains their inalienable rights. This study focused on ways of managing the higher education needs of internally displaced persons. Three hypotheses guided the study. The descriptive survey research design was adopted in the study. The entire population was used since it is not large enough to warrant randomization. The sample comprised 600 participants (38 teachers and 562 senior secondary (SS 1-3) students from three secondary schools. The instrument used for data collection was questionnaire titled Higher Education Needs for Displaced Persons Questionnaire (HENDPQ) on a modified four point Likert scale. The precision and internal consistency of the instrument was determined using Cronbach reliability method which gave rise to a coefficient ranging from 0.76 - 0.81. The data was collected personally by the researchers with the help of three research assistants. The data collected was analyzed using Pearson Product Moment Correlation Analysis at 0.05 level of significance. The result of the analysis revealed that there is a significant positive influence of higher education needs on development of displaced persons, provision of scholarships/bursaries on access to higher education; and provision of certified distance learning and e-learning opportunities on access to higher education for displaced persons. Based on these findings it was recommended that the government should urgently manage the higher education needs of displaced persons through adequate provision of scholarships/bursaries, distance learning and e-learning opportunities to enhance access to higher education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".