Editorial: Systemic Perspectives on New Alignments During COVID-19: Digital Challenges and Opportunities
Bibliographic record
Abstract
This overview of the articles presented in this issue considers the digital challenges and opportunities of the systemic perspectives on new alignments resulting from the onset of the COVID-19 pandemic. New challenges and opportunities were addressed by the 13 working groups of EDUsummIT2019 prior to the pandemic. However, the evidence and analyses presented in this issue have built on those originally identified perspectives by reviewing recent (2020/2021) research, development and practice across many educational sectors and contexts. We have shown that the status quo in the majority of education systems across the world has been thrown out of kilter. This has resulted in new alignments needing to be made to take account of the enforced remote learning when schools have been closed and blended learning has become widely practised even at school level. The most prominent of these have been caused by changes in digital equity which consequently imposes new challenges to policy makers, teachers and learners. This special issue stimulates reflection in and on practice as well as help problematizing new research challenges.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".