Fostering A Remote Cohort Community of Graduate Student Peers During The COVID-19 Pandemic
Bibliographic record
Abstract
Within this article, the authors describe a “cohort community” that was born through a desire to create a space for graduate students at both the MA and PhD level to thrive in their respective programs. As global circumstances closed campuses, the cohort community was forced to shift into digital spaces, bringing with it new opportunities for growth and connection. Herein, each author reflects on these opportunities as well as the vulnerability and trust called for in the graduate student experience in general and during times of crisis. This community fostered active feedback between graduate students, networking with other budding scholars, comradery in learning environments, support between students going through graduate school together, and accountability of progression towards program milestones. Through the reflections of the authors, we discuss the facets of the community that gave it strength and present a series of recommendations regarding the future of digital graduate student cohort communities and the possibilities of other such communities on different campuses. Keywords: Community, Collaboration, Cohorts, Graduate education, Duoethnography, COVID-19
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".