Exploring teachers’ public interactions and private conversations during the pandemic: A qualitative study using Goffman’s dramaturgical theory
Bibliographic record
Abstract
This study aimed to better understand teachers’ public interactions and private conversations during the shift to remote learning after the onset of the Covid-19 pandemic. Using Erving Goffman’s dramaturgical theory to frame my research, I conducted interviews with eight full-time teachers who had experience teaching in a classroom setting, prior to and during the pandemic. Erving Goffman, an underappreciated and poorly understood Canadian sociologist was interested in making sense of human interaction. To do so, he relied heavily on the metaphor of the theatre, likening human interaction to an actor giving a performance on stage for their audience. In order to give the most successful performance possible, an actor may utilize a variety of tools of the theatre to help convince their audience that the actor is who they profess to be. With this in mind, participants were asked to share their experiences interacting with colleagues, students, and administrators from the perspective of their professional interactions and private conversations during the shift to remote learning. The findings of this study suggest that Goffman’s dramaturgical theory, specifically his understanding of front stage and back stage performances, helps to illustrate the ways in which teachers were required to engage in impression management while teaching during the pandemic. More specifically, the findings showed that teachers struggled to remain upbeat and animated and show humility while adjusting to the challenges of teaching students virtually. Teachers also found it challenging to act professionally and to support their colleagues during the pandemic. Additionally, while being outwardly supportive of their administration and their decisions during this time, they also found ways to cope inwardly with those decisions. Lastly and perhaps most importantly, teachers talked about their students a lot and expressed significant concerns for their overall well-being.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".