Legal Analysis of Gender Disparities in Engineering: Perspectives from Engineering Law and Reform Pathways
Bibliographic record
Abstract
Gender inequality in the engineering profession remains pervasive, with Nigeria exemplifying entrenched disparities despite global and domestic equality frameworks. Women account for less than 12 per cent of registered engineers in Nigeria, reflecting a significant underrepresentation that persists notwithstanding constitutional guarantees, statutory prohibitions on discrimination, and ratified international instruments such as the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW). This paper interrogates the persistence of inequality through feminist legal theory and intersectional analysis, situating engineering law at the intersection of gender justice and environmental governance. It combines doctrinal analysis with empirical material, including case studies, policy reviews, and interviews, to reveal the misalignment between formal norms and substantive outcomes. Findings demonstrate that weak enforcement, regulatory inertia, and cultural norms perpetuate occupational segregation, while professional licensing frameworks operate in gender-neutral but exclusionary ways. Comparative insights from South Africa, Norway, and Canada demonstrate that robust enforcement of equality norms, quota systems, and gender-responsive regulatory measures can redress systemic exclusion. The paper proposes a multi-level reform strategy encompassing legislative reform, stronger oversight, institutional capacity-building, and integration of gender equity into environmental and engineering regulation. By linking Nigeria’s environmental jurisprudence with engineering governance, this study advances both scholarly discourse and practical frameworks for dismantling structural barriers and promoting inclusive development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".