Examining Canada’s Scientific Literacy Through COVID-19 Tweets
Bibliographic record
Abstract
Scientific misinformation spread on social media is a concern for science communicators, health communicators, and science educators alike. During the COVID-19 pandemic, the World Health Organization (WHO) released a statement that modern technology has created an infodemic, undermining the COVID-19 response effort. Misinformation spread online threatens public health and can endanger lives. So how do we combat it? The leading solution is education, in particular, equipping individuals with scientific literacy. Scientific literacy, or the ability to critically evaluate, understand, and make decisions regarding scientific information, is the goal of science curriculums globally. There has been much research over the past couple of decades regarding the usage of scientific literacy in formal learning environments. In contrast, the relationship between scientific literacy and online informal learning environments such as social media is not well understood. Our case study sought to help fill this gap in the research by exploring how Canadians employ scientific literacy on Twitter—a popular social media site—when discussing the COVID-19 pandemic. We conducted an exploratory qualitative case study exploring 2 600 tweets originating from accounts with user locations in Canada and shared on Twitter during the first ten months of the pandemic (March 2020 to December 2020) to see whether and how they displayed scientific literacy. In addition, we examined the trends and factors that affect the usage of scientific literacy online. Using qualitative content analysis techniques and supplemental statistical analysis, we found that 10% of tweets sampled displayed scientific literacy, while 2% did not exhibit scientific literacy. There were no interprovincial differences in how Canadians displayed scientific literacy, with all provinces sampled exhibiting scientific literacy in approximately 10% of tweets. Furthermore, scientific literacy was not affected by how often the user tweeted, how many followers they had, or the month the tweet was shared. We discovered a strong relationship between the tweet's topic and if it displayed scientific literacy or a lack of scientific literacy. Our study provides more insight into how scientific literacy is displayed online. Future researchers can use this as a starting point to conduct studies exploring how scientific literacy is employed in online spaces in different locations and contexts globally.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".