Bibliographic record
Abstract
“They (pandemics) are the original social and political disruptors, and sometimes that can be really positive” Brown (2020) in University of Hawai'i News. Most active teachers, in the past year, have taught both in-person and on-line. Using the Cynefin Framework, a decision-making framework which is based in complexity science (Snowden & Boone, 2007, p.70), I examine the pedagogical changes made by teachers in the emergency teaching situation brought about by the COVID-19 pandemic. When using the Cynefin Framework, issues facing an organization can be categorized in one of five possible contexts, simple, complicated, complex, chaotic and disorder, depending upon the severity of the disruption. In a regular classroom most disruptions would be in the simple or complicated context and teachers will solve those problems by making decisions based on prior practice. In the complicated contexts, the same teachers may seek advice from senior teachers, or experts, to solve situations. A problem in the complex context would require the teacher or administrator to find an emerging path through the situation. The disruption I am examining is, according to Brown, the original disruptor—a pandemic, namely COVID-19 which caused school instruction to move from face-to-face to emergency on-line teaching. While much of the beginning on-line teaching began in a chaotic context, that is not the only category that is identified by use of the Cynefin Framework. With data drawn from three interviews given by teachers or administrators recorded on YouTube for the Global Teacher Prize dating from March 2020 forward, I examine how both teachers and schools are changing their use of technology. I look at changes and modifications to pedagogy that the teacher has instituted and have determined work. I will then determine if the change is primarily beneficial for the teacher, student, or another party—uncovering the silver linings and innovations in the ways that teachers have changed their use of technology and their pedagogy during the emergency on-line teaching of COVID-19. Reference Snowden, D. J., & Boone, M. E. (2007). A Leader’s Framework for Decision Making. Harvard Business Review, 1–25. University of Hawai'i News. https://www.hawaii.edu/news/2020/04/07/covid19-vs-spanish-flu/
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.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".