Dialogic & Critical Pedagogies: An Interview with Ira Shor
Bibliographic record
Abstract
In 2016, the Main Editors of Dialogic Pedagogy Journal issued a call for papers and contributions to a wide range of dialogic pedagogy scholars and practitioners. One of the scholars who responded to our call is famous American educator Ira Shor, a professor at the College of Staten Island, City University of New York. Shor has been influenced by Paulo Freire with whom he published, among other books, “A Pedagogy for Liberation” (1986), the very first “talking book” Freire did with a collaborator. His work in education is about empowering and liberating practice, which is why it has become a central feature of critical pedagogy.Shor’s work has touched on themes that resonate with Dialogic Pedagogy (DP). He emphasises the importance of students becoming empowered by ensuring that their experiences are brought to bear. We were excited when Shor responded to our call for papers with an interesting proposal: an interview that could be published in DPJ, and we enthusiastically accepted his offer. The DPJ Main Editors contacted the DPJ community members and asked them to submit questions for Ira. The result is an exciting in-depth interview with him that revolved around six topics: (1) Social Justice; (2) Dialogism; (3) Democratic Higher Education; (4) Critical Literacy versus Traditional Literacy; (5) Paulo Freire and Critical Pedagogy; and (6) Language and Thought. Following the interview, we reflect on complimentary themes and tensions that emerge between Shor’s approach to critical pedagogy and DP.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.041 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.041 |
| 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".