Same storm, different nightmares: emergency remote teaching by contingent communication instructors during the pandemic
Bibliographic record
Abstract
The COVID-19 pandemic amplified existing inequities in higher education. This paper documents the stories of four precariously employed communication instructors in their transition to emergency remote teaching in March 2020. Through collaborative autoethnography, the instructors share their stories of reliance and compliance within the gig academy, using their support networks to foster resilience and create points of resistance. In the Spring 2020 semester, we experienced the same storm but with different nightmares. Technological frustrations, mental health concerns, accent barriers, financial stresses, care work, and illness were pushed to the background while we dealt with suddenly teaching online during the pandemic. The relentless uncertainty about job security hanging overhead persists. From our subaltern counterpublic, we posit a resistance to the gig academy. We urge departmental leadership to use this paper to inform policy making and practice and for other contingent instructors to expose their stories in scholarship.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| 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".