Investigating understandings of critical literacies among Finnish and Canadian teachers
Bibliographic record
Abstract
Critical literacy has been defined as the use of analog and digital materials towards questioning and deconstructing problematic and oppressive societal norms and further reconstructing more socially just narratives. Even though critical literacy is a well-known concept in many English-speaking countries, its application and significance in Europe have not been sufficiently investigated. As a result, this dissimilar study of the concept can be reflected in teachers’ understandings of it in theory and in practice. This contribution focuses on exploring and reconstructing teachers’ perspectives of critical literacies comparatively. More specifically, the article highlights similarities and differences in the way teachers from Canada and Finland think of the definition and implementation of critical literacies in their own situated, socio-cultural and socio-educational contexts. The study is based on theory-generating expert interviews and a comparative case study design, and the analysis of the data follows a grounded theory framework. The main results show a considerable convergence in perspectives; while Canadian teachers explored connections of critical literacies with social justice education, Finnish teachers rather highlighted ideas of information management and multiliteracies. Nevertheless, there were noticeable connections among these perceptions which are relevant for the development of the field and are further explored in the discussion part of this article.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".