The Fecundity of Silence in Dialogue: Students’ Experiences of Classroom Discussions
Bibliographic record
Abstract
Scholars have noted that literary studies ought to be marked by opportunities for students to engage with difficult topics. Arguably, class discussions are a signature pedagogy of literary studies that support engagement with difficult topics. However, silences can disrupt the anticipated insight of such discussions. How do students in higher education literary studies experience silence in such discussions? Can student silence be understood as a generative aspect of difficult conversations about literary texts? This research sought to disrupt established beliefs about the idea of silence as non-participation and to determine whether there is evidence that speaks to the fecundity of silence in discussions. Silence has not been studied nearly as much as dialogic pedagogy, but scholars recognize its value for literacy. My research is based on the conceptual frameworks of dialogic pedagogy and hermeneutics and methodologically uses hermeneutic interviewing to develop thick descriptions of students’ experiences. This presentation focuses on my preliminary findings with reference to the academic literature on student silence in dialogic teaching and learning contexts. I address confirmations and disconfirmations of prevailing academic perspectives and instructors’ assumptions that consider student silence as fecund. This research is relevant for instructors in literary studies but may have implications for other disciplines using dialogic pedagogy.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".