Artifactual Critical Literacy: A New Perspective for Literacy Education
Bibliographic record
Abstract
In this article, we propose a framework for literacy education, called artifactual critical literacy, which unites a material cultural studies approach together with critical literacy education. Critical literacy is a field that addresses imbalances of power and, in particular, pays attention to the voices of those who are less frequently heard. When critical literacy education is joined with a material cultural studies approach, which holds that cultural “stuff” (Miller, 2010) matters as a form of expression and also as embedded cultural practice, literacy practices such as hip hop and vernacular literacies are then given more attention alongside canonical texts. Stories connected to objects and home experience can provide a platform and starting point for text-making. Text-making can also be set within a framework that is multimodal and allows for a much wider concept of meaning making. In this article wecombine practical examples with a new theoretical framework that brings these traditions together.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".