Women’s role in nation building: socialising Saudi female preservice teachers into leadership roles
Bibliographic record
Abstract
This paper is about socialising Saudi female preservice teachers (PSTs) while they are attending university into the role of educational leadership. This leadership role should be broadened to include nation building per the tenets of Saudi Arabia’s national development plan, Vision 2030. After discussing nation building and profiling the Saudi educational context (including educational reform initiatives), and after explaining Islamic understandings of educational leadership (values and traditions), an overview of the intentionally planned professional socialisation process is presented. The paper culminates in ideas around what an aligned curriculum might contain so Saudi female PSTs are exposed to educational leadership for nation building while at university. With intentional socialisation into this role, upon graduation, they should be more inclined to assume a role in nation building by (a) influencing the educational sector, players, and policies to benefit the nation and (b) convincing other sectors of the value of women and the education sector in ensuring an ambitious nation. Insights apply to other nations engaged in nation building including Arab nations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".