“I Love this Approach, But Find It Difficult to Jump in with Two Feet!” Teachers’ Perceived Challenges of Employing Critical Literacy
Bibliographic record
Abstract
Accompanying myriad definitions of critical literacy is an absence of pedagogical models for implementing critical literacy in teacher education contexts. This action research explores critical literacy with pre-service and in-service teachers in teacher education courses offered in the United States. The primary data sources include online weekly discussions on course readings in the TESOL methods courses I taught in Hawaii and Kansas. First, I propose the working definition of critical literacy in the study (Luke, 2012) and then present course participants’ perceived challenges of employing critical literacy in their current and future classrooms. Findings reveal that despite the differences in the two instructional contexts, both groups recognized that the current standards-based, test-driven educational environment would be the major obstacle for enacting critical literacy in their classroom. In addition, the lack of understanding of critical literacy was addressed by both groups of teachers. I also discuss my struggle and dilemma as a critical teacher educator. Finally, this article concludes with suggestions for introducing critical literacy in teacher education contexts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".