Teaching in the COVID-19 era: Understanding the opportunities and barriers for teacher agency
Bibliographic record
Abstract
The school closures necessitated by the COVID-19 pandemic created a rapid shift to alternative modes of educational delivery, primarily online learning and teacher-supported home-schooling. This shift has revealed deep inequities in education systems worldwide, as many children lost access to teachers and schooling. An effective response to these changes has tested teachers’ personal capacities and individual and collective agency intensely. The research lab we report on within this paper aimed to develop a better understanding of teacher agency in meeting the challenges of the pandemic and the physical and relational enablers and constraints of their environment. Drawing on case study reports from six international contexts and a series of online discussions with research lab participants, this study explores teachers’ enactment of agency in the context of various circumstances and environments. The authors argue that it is imperative that education systems support the enhancement of teachers’ personal and collective agency in the face of continued disruption to schooling and ongoing challenges to educational equity.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".