Chat Transcripts in the Context of the COVID-19 Pandemic: Analysis of Chats from the AskAway Consortia
Bibliographic record
Abstract
Objective – During the COVID-19 pandemic, the majority of post-secondary institutions in British Columbia remained closed for a prolonged period, and volume on the provincial consortia chat service, AskAway, increased significantly. This study was designed to evaluate the content of AskAway transcripts for the 2019-2020 and 2020-2021 academic years to determine if the content of questions varied during the pandemic. Methods – The following programs were used to evaluate the dataset of more than 70,000 transcripts: R, Python (pandas), Voyant Tools and Linguistic Inquiry and Word Count (LIWC). Results – Our findings indicate that the content of questions remained largely unchanged despite the COVID-19 pandemic and the related increase in volume of questions on the AskAway chat service. Conclusion – These findings suggest that the academic libraries covered by this study were well-poised to provide continued support of patrons through the AskAway chat service, despite an unprecedented closure of physical libraries, a significant increase in chat volume, and a time of global uncertainty.
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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".