An investigation of campuses’ sustainability practices in Nigerian higher education institutions
Bibliographic record
Abstract
Purpose This study aims to dive into the unique context of Nigerian universities, exploring their roles in terms of campus sustainability practices and the challenges they face while implementing sustainability initiatives. Design/methodology/approach This study investigates sustainability practices through in-depth interviews with higher education institutions (HEIs) in developing countries. Experts from eight different government-owned universities in the Southwestern region of Nigeria participated in this study through a purposive sampling technique. The study leveraged the Sustainability Tracking and Rating System framework to determine potential sustainability management indicators tailored to the Nigerian context. Findings The findings reveal a limited degree of engagement and implementation and show that HEIs adopt a wide range of sustainability approaches. Hence, underlying the necessity for concerted efforts to enhance sustainability initiatives in Nigerian HEIs. Originality/value To the best of the authors’ knowledge, no previous studies have investigated Campuses’ Sustainability Practices in Nigerian HEIs. This study contributes to the body of literature by clarifying the challenges faced by Nigerian HEIs as their comprehension of sustainability practices widens, which has gotten little attention in previous literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".