The African Storybook and Language Teacher Identity in Digital Times
Bibliographic record
Abstract
The African Storybook (ASb) is a digital initiative that promotes multilingual literacy for African children by providing openly licenced children's stories in multiple African languages, as well as English, French, and Portuguese. Based on Darvin and Norton's ( ) model of identity and investment, and drawing on the Douglas Fir Group's ( ) framework for second language acquisition, this study investigates Ugandan primary school teachers’ investment in the ASb, its impact on their teaching, and their changing identities. The study was conducted in a rural Ugandan school from June to December 2014, and the data, which focus on one key participant, Monica, were drawn from field notes, classroom observations, interview transcripts, and questionnaires, which were coded using retroductive coding. The findings indicate that through the ASb initiative and its stories, Monica and other teachers began to imagine themselves as writers, readers, and teachers of stories, reframing what it means to be a reading teacher. Teachers’ shifts of identity were indexical of their enhanced social and cultural capital as they engaged with the ASb, notwithstanding ideological constraints associated with mother tongue usage, assessment practices, and teacher supervision. The authors conclude that the enhancement of language teacher identity has important implications for the promotion of multilingual literacy for young learners in African communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".