Seeking serendipity: teacher educators as adaptive experts during COVID
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted and dismantled conventional models of teaching and learning within teacher education programmes across British Columbia, Canada. Along with the challenges encountered by teacher education programmes, these circumstances have also catalysed long-overdue changes to traditional and colonial educational structures, policies, pedagogies, and practices. Through the analysis of conversations with eight diverse teacher educators and leaders from five universities, this study investigated change and continuity within the landscapes of teacher education during the first 2 years of the pandemic, including substantive systemic changes made to institutional policies, pedagogical approaches, and professional learning models as an intentional response to the challenges brought forward by Covid-19. Informed by the scholarship on serendipity and zemblanity, we explored surprising discoveries, as well as unfortunate outcomes that resulted by design during the pandemic response. Reframing the current crisis as an opportunity to re-vision teacher education, we examine the fundamental importance of becoming adaptive experts; educators who have the capacity to respond effectively to unpredictable, complex, and ever-evolving educational environments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it