Using a multiliteracies approach to foster critical and creative pedagogies for adult learners
Bibliographic record
Abstract
Drawing upon a pilot study and a Social Sciences and Humanities Research Council (SSHRC) Insight research study to explore how a multiliteracies framework may inform more critical and creative pedagogical approaches for adolescents and adults, this article begins with a brief overview of the literature on multiliteracies and then overviews the methodology used in the two research studies. Although multiliteracies has not been used frequently as a theoretical framework to inform work in adult learning contexts, this article argues that there are many benefits to this approach for adult educators to consider, particularly given the increasing need to attend to learning issues pertaining to globalization, diversity, and the impact of new technologies. Data from the interviews are combined with an analysis of the literature to explore the benefits offered by a multiliteracies approach by considering four main areas: lifelong learning and multimodalities; opportunities for engagement for English as Additional Language learners; new digital technologies and multiliteracies; and multiliteracies’ emphasis on social justice. The article concludes with a consideration of the potential for multiliteracies to inform educators working in a range of adult learning contexts.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| 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".