Exploring the importance of backstage conversations in student-faculty partnerships
Bibliographic record
Abstract
In the midst of ongoing global disruption, uncertainty, and loss caused by the COVID-19 pandemic, we embarked together on a student-faculty research partnership that developed as part of a larger multi-institutional research team.Our focus here is on the experience of the authors-a student and a faculty member.Drawing on two significant moments in our partnership, we use the concept of "backstage conversations" to explore how we handled uncertain/complex situations and the multifaceted dimensions of power in our research project.We suggest there is value in extending Erving Goffman's (1959) idea of backstage conversations to explore experiences in student-faculty partnerships and understand how they can help develop partnerships that occur within larger collaborative projects.The concept of backstage conversations comes from Goffman's (1959) dramaturgical framework, using the metaphor of theatre and performance to understand how individuals act and interact in different contexts.Goffman's framework references positions on a stage (frontstage, sidestage, and backstage), with backstage conversations usually characterized as being private: a space where individuals can relax and drop what might be considered "formal" role performance.Our process of reflection included us individually writing for 5 minutes to a prompt-What has this partnership been like for me?-and sharing with one another what we noticed (rather than what we liked/disliked) in each other's writing, allowing us to see what resonated with us as readers.We did this at the end of our partnership experience, which enabled us to identify words, phrases, and ideas that we felt were powerful and piqued our curiosity to know more.We then engaged in follow-up freewriting for 20 minutes and shared what we noticed again.From this process, we identified the topic of this reflection: the importance of backstage conversations in partnership spaces.To preserve each other's perspectives and voices, we approach this reflective essay by writing alternately in the first-person singular ("I") and the firstperson plural ("we").
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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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.032 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".