An Inquiry-Based Approach to Critical Literacy: Pedagogical Nuances of a Second Grade Classroom
Bibliographic record
Abstract
This case study explores the pedagogy and practices of an elementary school teacher who combines inquiry pedagogy and critical literacy. The authors gathered data for this analysis by conducting two interviews with a classroom teacher and observing classroom practices 12 times over a 6 month period. Through a general inductive approach to analysis, trends emerged that showed the classroom teacher used practices that combined traditional inquiry pedagogy for critical literacy development. This research provides insight into how this elementary teacher negotiated and connected inquiry to critical literacy. Furthermore, the findings can inform scholars and teacher educators of successful teaching strategies as they prepare future generations of elementary teachers. Cette étude de cas explore la pédagogie et les pratiques d’une enseignante à l’élémentaire qui allie pédagogie d’enquête et littératie critique. La cueillette de données s’est faite par deux entrevues auprès d’une enseignante titulaire et douze observations en salle au cours de six mois. Une approche inductive à l’analyse a révélé des tendances indiquant que l’enseignante employait des pratiques qui intègrent une pédagogie d’enquête traditionnelle au développement de compétences essentielles à la littératie. Cette recherche donne un aperçu des démarches de l’enseignante pour négocier et lier enquête et littératie. De plus, les résultats présentent aux chercheurs et aux formateurs d’enseignants des stratégies pédagogiques efficaces et fructueuses dont peuvent profiter les générations futures d’enseignants à l’élémentaire.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".