Graduate Students’ Exploration of Opportunities in a Crisis
Bibliographic record
Abstract
The following white paper details the University of Calgary’s 2021 graduate student conference titled, ‘Opportunities in a Crisis.’ This white paper works to describe how graduate students explore the terms ‘opportunities’ and ‘crisis’ within their research interests. These research interests were interdisciplinary to various fields such as telecommunications policy, algorithmic studies, critical race theory, and video game studies to list a few. Through this conference, we observed an acute awareness of the ways in which the COVID-19 crisis has impacted research in media activism, feminist media studies, internet infrastructure, and teaching and learning, to mention a handful. This white paper is divided by panel sections, thereby allowing readers to connect with this graduate student conference and help inform future research on topics in communication and media studies, as they are framed in working through these crisis moments in our global history. Our white paper set out to achieve two goals: first, document the presentations and emerging scholarly work of graduate students; and second, reflect on how research can, and very well does, pivot in times of crises, specifically using our current global COVID-19 pandemic as an ongoing, lived experience. This white paper achieves these goals which we believe helps in the preservation of this unique moment in time to be a graduate student.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".