Promoting global alliances for sustainable architectural education, training, and practice in Nigeria
Bibliographic record
Abstract
Abstract The field of architecture in Nigeria is experiencing dynamic growth and development, driven by urbanisation, infrastructure demands, and a growing awareness of sustainability. To navigate these challenges and contribute to sustainable development, the study aimed to explore the potential of global alliances that enhance architectural education, training and professionalism. The study combines literature reviews, six case studies of successful international architectural alliances and 34 interviews with architectural professionals to explore the significance of promoting global alliances for sustainable architectural training, practice, and profession in Nigeria. The findings suggest that many countries, like the United Kingdom, Australia, New Zealand, Canada, the United States of America, and Hong Kong, have formed alliances with architectural regulatory bodies in other nations to simplify international architectural practice. The benefits of a global architectural alliance enable architects to practice worldwide, promoting reciprocity and enhancing competitiveness and professional enhancement. Such alliances enrich architectural education, fostering the development of well-rounded professionals capable of addressing the complex challenges of the built environment. This research provides valuable insights for architectural education, training, and professional stakeholders seeking to elevate sustainable development in Nigeria. By cultivating global alliances, Nigeria can develop a thriving architectural landscape that addresses societal challenges, preserves cultural heritage, and leads the way in sustainable architectural practices.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".