Empowering Children Through a Global Education Reading Program
Bibliographic record
Abstract
This 19-year global education curriculum project was conducted by a Canadian researcher in solidarity with a Tanzanian women’s rights activist who requested assistance with her campaign to end the continued illegal practice of female circumcision in her village. Years of fundraising, researching and planning resulted in filling a primary school with multicultural children’s literature to be used in a daily global reading program from grades one to seven. During the seven years of teacher training and curriculum implementation workshops in Tanzania involving global education and holistic education principles, the teachers expressed their shock over the unprecedented openness and critical thinking of their students. The students developed a keen awareness of social justice issues through their daily reading and book talks. By the upper grades, the male and female students articulated their stance against female circumcision without controversy, as though it was common knowledge that this was an illegal and unacceptable practice. Additionally, the students scored top results on their graduating national exams. The intent of the project was not to improve standardized testing results of the most disadvantaged, rural students and HIV orphans. This was a positive side-effect of the transformative powers of storytelling in the lives of children who are now better equipped to think critically about illegal, harmful cultural 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".