A Socio-cultural Perspective on School-based Literacy Research: Some Emerging Considerations
Bibliographic record
Abstract
Much research on reading and writing in schools continues to focus on individual cognitive skills. In contrast, investigations of literacy-learning in out-of-school settings have often taken a socio-cultural perspective, situating reading and writing in social relations and cultural institutions. The last 20 years have seen a proliferation of studies documenting the ways in which printed texts are taken up in a wide variety of settings from after-school clubs and community-based adult literacy programmes to workplaces, the Internet, and ‘everyday life’. Increasingly, there have been calls for sociocultural literacy researchers to begin directing their attention to mainstream educational contexts. In this paper, we join in and seek to contribute to such calls by drawing out some of the complexities and caveats that also need to be kept in mind. After briefly reviewing what it means to define literacy and learning in relation to socio-cultural context, we explore some recent arguments for conceptual and methodological refinements. We then turn our attention to schools and to what a socio-cultural definition of literacy has to offer in terms of addressing diversity and educational inequity, and we draw out several issues that require closer consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.015 | 0.063 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".