Post-Pandemic University Timeline Project
Bibliographic record
Abstract
In early 2020, the start of the covid-19 pandemic brought in-person post-secondary learning to a abrupt halt. Over the last two years, universities across Canada have worked to follow public health orders and preserve the university experience as case counts and variants continued to disrupt our everyday lives, closing and opening campuses with little warning. Throughout the pandemic, universities communicated virtually with students about a variety of topics including "emotional support and building a shared experience", "government mandates and tracing cases", "learning modality", "spaces of facility, operations, and strategy" as well as "student services". In order to better understand the correlation between the time period of each COVID-19 pandemic wave in Canada and the frequency of each category of university response, a timeline covering the period from January 2020 to April 2022 was created.\nThe goal of creating this timeline is to connect university responses to the context of the pandemic, regionally, nationally, and globally. In doing this, we seek to understand and speak to trends that may be uncovered in terms of the types of responses. Three universities communications and responses were organized, dated and coded using Dedoose. The three university responses that were analyzed (UBC, UAlberta and Western) will be used as the initial phase of the research in order to create a platform that can be built upon to complete the full U-15 dataset.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".