Linking digital, visual, and civic literacy in an era of mis/disinformation: Canadian teachers reflect on using the Questioning Images tool
Bibliographic record
Abstract
The spread of mis-and dis-information during elections creates an opportunity and an imperative to cultivate and develop critical civic literacy with young people.Leading up to the 2019 Canadian federal election, researchers worked with Canadian nongovernmental organisation (NGO) CIVIX to translate research on visual media literacy into an innovative and timely teaching resource: Questioning Images.This paper explores what teachers' responses to using this particular resource can highlight about the links between visual literacy, digital literacy, and civic literacy, to support critical digital citizenship education.After setting up the background to the study, we present key themes from focus groups with teachers who used the resource and then consider implications.Overall, we found the tool supported teachers in deepening their understanding of, and approach to, digital literacy and highlighting the importance of visual literacy, and it supported political education and civic literacy during and beyond the 2019 election.We argue, however, that further resourcing is needed to support a comprehensive approach to visual culture where digital, visual, and civic literacies are mutually constitutive and where visual analysis goes beyond verification to offer ways of understanding visual disinformation in terms of its broader civic implications.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".