Bibliographic record
Abstract
The following case study is drawn from a Pashtun family of 31 people living together in a house in Kabul, the capital city of Afghanistan, and was collaboratively researched with a member of the household. Afghanistan has one of the world’s lowest literacy rates, at 28.1 % literacy (UNICEF, 2004). Finding a way to take into consideration “the symbolic and material transactions of the everyday provide the basis for rethinking how people give meaning and ethical substance to their experiences and voices” (Giroux and Simon, 1989), rings true in the context of Afghanistan, where an ambitious agenda for raising access toeducation and literacy must find roots in the existing culture, coping mechanisms used by families, and the limited literacy and learning resources to which they have access. The issues brought to light in this case study suggest that validating Afghanistan’s literary traditions holds potential for empowering new learners, tapping into literacy practices supported by family networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".