Emotions in Pedagogical Practice: Relational Ethics and Collectivity Building in W2B
Bibliographic record
Abstract
Emotions and relationality can serve important pedagogical purposes.Paying close attention to the ways in which emotions are implicated in our pedagogical practice can aid in the development of connections with and between students, which contributes to fostering a sense of collectivity in the carceral classroom that encourages students to learn from and with one another.We situate this as a form of relational ethics, which we exemplify using the fi ve R's (respect, relationships, relevance, reciprocity, and responsibility) identifi ed by Tessaro and colleagues (2018), and by drawing on our autoethnographic refl ections and emotional experiences as Walls to Bridges (W2B) instructors and student alumni (both inside and outside).Adopting a relational ethics approach to teaching and learning enables us to better identify the fault lines in how students are taking up the literature that is being studied together in relation to their own histories and lived experiences, which can lead to 'teachable moments' that foster dialogical exchanges amongst students.By embracing relational ethics, we suggest that the W2B educational model has the potential to build collectivity amongst students and instructors that transcends the carceral classroom and continues to impact participants both personally and professionally, long after the course has ended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".