Theoretical approaches to literacy: Evidence from America, Canada, England, Singapore, Senegal, Indonesia and Iran’s curriculum
Bibliographic record
Abstract
Educational planning has traditionally taken the form of determining how to inculcate literate capabilities in as wide a segment of the population as possible. The key planning dimension of education, then, has been to determine first, the definition of literacy and second, how best to provide as much literacy training as possible within the resources available to the country. Then, curriculum planning shapes the literacy and determines how to convey it among the people in the society. Literacy has many different definitions. These definitions are influenced by the “autonomous” and “ideological” approaches to literacy. The “autonomous” approach to literacy disguises the cultural and ideological assumptions that underpin it, so that it can then be presented as though they are neutral and universal. On the other hand, the alternative, ideological approach to literacy, offers a more culturally sensitive view of literacy practices as they vary from one social-cultural context to another. This approach posits that literacy is a social practice, not simply a technical and neutral skill. In this paper, the theoretical approach underlying the definition of literacy in the curriculum of two developing countries (Indonesia and Senegal), four developed countries, i.e., America (the state of New Jersi) , Canada (the state of Alberta), England and Singapore, and finally the curriculum of Iran is discussed. We tried to clarify that the curriculum of these countries especially Iran’s are to what extent influenced by the “autonomous” and “ideological” approaches to literacy.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".