Bibliographic record
Abstract
For the educator interested in such topics as how to engage children in becoming more fluently literate, Vygotsky has offered a crucially important insight. Before his work – and, of course, still commonly the case for those who have been unable to see its richer implications for education – approaches to education generally have tended to take one or more of three general approaches. We will sketch them very briefly and then indicate in what way Vygotsky's insight into the role of cognitive tools helps us to transcend the limitations of the three traditional approaches. The main purpose of our chapter, however, is to explore some new implications of Vygotsky's insight, seeking to unfold it in ways that enable educators to discover new pathways to engage students in literacy successfully. We think, also, that this analysis of the cognitive tools that are constituents of literacy provides a novel expansion of Vygotsky's insight in ways directly applicable to education. THREE TRADITIONAL CONCEPTIONS OF THE EDUCATOR'S TASK The first, and most ancient, conception of the educator's task is to engage the young learner in what today we call an apprenticeship relationship with an expert. The child would, consequently, learn by doing with an expert on hand to guide and correct the novice. This kind of learning has been perhaps the most common in human cultures across the world and was almost the exclusive mode of instruction in hunter–gatherer societies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".