Leading in Extraordinary Times: Documenting Pandemic Stories through Graphic Recording
Bibliographic record
Abstract
This field report documents—through the use of graphic recordings—the “Leading in Extraordinary Times” webinar series that ran throughout the COVID-19 pandemic. Hosted by a team in the School of Leadership Studies at Royal Roads University in British Columbia, Canada, this series was notable for its use of a graphic recorder who joined each webinar and produced an image drawn in real time. We felt it was essential to document stories from this historical moment, not only through words and audio-visual recordings, but also through images. Following the webinar, each image was shared with participants, along with the video recording. At that time, free special topic webinars open to the public were still relatively novel. The success of this webinar series demonstrated that people were yearning for meaningful ways to connect online to reduce isolation and continue learning as wave after wave of the pandemic hit. We, therefore, saw a role we could play in both engaging and building community while hosting relevant, helpful, and timely dialogues throughout the pandemic. A summary of each webinar topic, together with the graphics and links to webinar recordings, are included in the report.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.819 | 0.556 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.506 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.730 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".