Pracademics in the pandemic: pedagogies and professionalism
Bibliographic record
Abstract
Purpose This thinking piece examines, from the viewpoint of two Canadian pracademics in the pandemic, the role of pedagogy and professionalism in crisis teaching and learning. The purpose of the paper is to highlight some of the tensions that have emerged and offer possible considerations to disrupt the status quo and catalyze transformation in public education during the pandemic and beyond. Design/methodology/approach This paper considers the current context of COVID-19 and education and uses the professional capital framework (Hargreaves and Fullan, 2012) to examine pandemic pedagogies and professionalism. Findings The COVID-19 pandemic has catapulted educational systems into emergency remote teaching and learning. This rapid shift to crisis schooling has massive implications for pedagogy and professionalism during the pandemic and beyond. Despite the significant challenges for educators, policymakers, school leaders, students and families, the pandemic is a critical opportunity to rethink the future of schooling. A key to transformational change will be for schools and school systems to focus on their professional capital and find ways to develop teachers' individual knowledge and skills, support effective collaborative networks that include parents and the larger school community and, ultimately, trust and include educators in the decision-making and communication process. Originality/value This thinking piece offers the perspective of two Canadian pracademics who do not wish for a return to “normal” public education, which has never serve all children well or equitably. Instead, they believe the pandemic is an opportunity to disrupt the status quo and build the education system back better. Using the professional capital framework, they argue that it will be educators' professionalism and pandemic pedagogies that will be required to catalyze meaningful transformational change.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.043 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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 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".