Responding to COVID-19: Contextual, Pedagogical, and Experiential Considerations from Canadian Northern Postsecondary Educators
Bibliographic record
Abstract
The COVID-19 pandemic forced the closure of face-to-face classes in a northern Canadian college in March 2020. Educators and staff went into rapid response mode to continue teaching and supporting students from a distance. Critical reflections were written by the authors to summarize their responses to teaching and learning during the early phases of the pandemic. These reflections were themed, considered individually and collectively, then analyzed and synthesized. In this paper, critical reflection is used as an educational process within the context of critical constructivism and transformative paradigms. We share how teaching during the pandemic solidified our commitment to students and cemented our critical pedagogy by thinking and acting critically to assist students with this disruption in their education. Equipped with these capabilities, educators are empowered to work with students to problem solve and transform our educative lives for a just society. An inter-professional opportunity across programs, spurred by the pandemic, meets organizational strategic directions and fosters a promising relationality. Increased territorial and local technological supports and internal professional development is needed to solidify the immense prospects for distance education as the College transitions to a polytechnic university.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.048 | 0.021 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".