Inoculating against an infodemic: microlearning interventions to address CoV misinformation
Bibliographic record
Abstract
The dataset was created as part of the CIHR-funded project, 'Inoculating against an infodemic: Microlearning interventions to address CoV misinformation'. This research examines digital misinformation flows pertaining to the 2020 COVID-19 pandemic for the purpose of developing educational interventions to reduce the spread of online misinformation. The dataset contains transcripts of 45 one-to-one, semi-structured interviews that were conducted in June/July 2020. These interviews were used to gather data about how Canadians engaged with COVID-19 information online. Two different sets of interview questions were used: 18 of the transcripts follow protocol A, and 27 follow protocol B. Both protocols asked the same initial questions about COVID-19 information habits. Protocol A then asked questions about interviewee-provided media samples, while protocol B asked questions about interviewer-provided media samples. Due to the sensitivity of the data this data set will only be available to vetted researchers upon request. To request access to the data, contact the lead author, Jaigris Hodson.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.017 |
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".