COVID Conversations: A Collaborative Self-Study of Four Teacher Educators
Bibliographic record
Abstract
This collective self-study chronicles the experiences and reflections of four women teacher educators living and working during the COVID-19 pandemic. Data collected between March 2020 and December 2021 centered on the following question: what were we, as teacher educators, experiencing professionally and personally as a result of the pandemic? The COVID-19 context presented a unique set of challenges and an additional layer of complexity highlighting intersections of policy, teacher education, and societal expectations. Findings connected to our professional identities as teacher educators included the need to reframe professional expectations and the impacts to historical narratives in teacher education. Personally, the complexity of mixing the personal and professional spheres and increased personal responsibilities emerged. Findings from this study reaffirm that when we have opportunities to access and learn from the lived experiences of others, we are better positioned to understand ourselves and the personal and professional roles we take on.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".