Conviviality in Nigerian higher education institutions: decolonizing the contents for cutting-edge research
Bibliographic record
Abstract
Given the state of polity and lack of recognition, we argue that there is a myriad of problems in Nigerian higher education institutions (NHEIs). These problems have impeded the progress of academia across all the thirty-six states of the country. In addressing research, teaching, community service, and the impact of NHEIs, we seek the tool of conviviality. By conviviality, we mean the state of incompleteness that challenges every individual and group to be open-minded in their relationships, claims, being, belonging, and articulations of identities. The paper employs a critical lens to understand the indefatigable tool of conviviality among researchers, academics, and analysts for promoting cutting-edge research. This is done specifically to address, propose, and re-energise the missing link that exists in modern Nigerian higher education institutions.
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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 | 0.050 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| 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".