Bibliographic record
Abstract
The vulnerability of women and girls in Northern Nigeria is likened to an ‘endangered species’ that struggles daily to survive. That region of the country is notorious in negativities: poverty, illiteracy, unemployment, insecurity and now, blatant abuse of health and reproductive rights of women and girls. This article, hence, exposes the precarity that pervades the region and highlights the factors above i.e., poverty, illiteracy, unemployment etc., and how women and girls have been made escape goats by the socio-cultural, economic and religious establishment in Northern Nigeria. Using statistics and scholarly findings of researchers and literature, the article articulates these factors, which include but not limited to, abuse of the health and reproductive right of women and girls, illiteracy and early marriage and insecurity. The article concludes by calling the government of Nigeria to treat the condition of women and girls in the Northeast as an emergency, by setting up structures headed by or headed by shared leadership roles of women, that will investigate their situations and proffer solutions. An Empowerment Education Fund should be created to provide accessibility for compulsory primary, secondary and even tertiary education to the girl child in Northern Nigeria.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".