Row-by-Column, Plexiglass & Zoom, Oh My! A K-12 COVID-19 Storm / A Pilot
Bibliographic record
Abstract
We are 21 years into the 21st century, and educational practices across North America were woefully unprepared to ‘flip the switch’ to online learning; at times no education occurred at all, not online or onsite. The COVID-19 pandemic disruptor storm peeled off the layers of blindfolds time accrued in an instant. Issues included three areas. Area one—unpreparedness: digital illiteracy relative to online learning and corresponding teaching models, equity issues pertaining to internet access and computer access, platforms that varied and were unreliable. Area two—inconsistent: (if any) guidelines on how to teach onsite, or those from a disease control group dictating a six-foot distancing, masks, plexiglass, and row-by-column with eyes facing forward (back to a 19th century teaching didactic model), and smaller class sizes. Area three-time/space continuum: the combining of online and onsite, teaching loads, and maintenance. This ‘alpha’ research study tried to capture a historic moment in time. A Human-centered Research Design (HcRD) protocol with three techniques to mitigate bias was used: (1) online survey, (2) focused interviews, and (3) crowd-sourced photographic content across two countries—USA and Canada as a convenience sample. The findings will reveal a ‘just-in-time’ snap shot of the tactics used pre- and current-, as well as ideas for post-pandemic—this research’s differentiator. The storm of COVID-19 played unprecedented havoc on schools across North America, but there are important learnings and these, along with some insights will be shared.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.332 | 0.122 |
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".