Concealing and Revealing: Information Design to Strengthen Civic Literacy in an Age of Digital Communication
Bibliographic record
Abstract
This thesis work responds to emerging issues in media and civic awareness in Canada, exploring how design can be used to highlight, examine and expose characteristics of the digital space in order to enhance young citizens’ media literacy. As the opening chapters will establish, younger Canadians are experiencing a unique combination of factors that render them insufficiently prepared to participate as digital citizens. These issues are compounded by digital threats to democratic values such as the rise in manipulative or propagandistic content, as well as intellectual silos created by algorithmic filtering. The title of the thesis, “concealing and revealing” speaks to our relationship with the digital space that is all at once present, immediate and yet, invisible, and elusive. My creative work aims to illuminate the invisible power dynamics perpetuated by digital tools using information design and data visualizations, presented through a large sculptural installation and a series of illustrated notebooks. The projects’ research focuses on teens and young adults, but the outcomes provide information that is pertinent to citizens of all ages. The projects rely on critical discourse analysis, semiotics and practice-led approaches to research. The theoretical framing of the projects apply Marshall McLuhan’s media theories to explore how we as a society may begin to evaluate our political experience in the digital age, in order to better understand the lasting impacts on citizenship and liberal democracy.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".