Bibliographic record
Abstract
In the past two decades, screen-based technology like smartphone devices has become ubiquitous within global society. These devices primarily function with visual displays and have allowed visual media to flourish. Visual literacy, the ability to critically consume visual material, is thus an essential competency to teach students for them to be able to navigate the increasingly visual world. The current and upcoming generations of students who grew up surrounded by technology like personal computers and smartphones are what some researchers call “digital natives.” They use visual forms of communication (emoticons, emojis, stickers, GIFs, and memes) to communicate online (in social media and messaging apps) instead of solely traditional word-based forms. However, being constantly bombarded by visuals growing up does not automatically equal visual literacy and, by extension, media literacy. Academic studies on university students’ visual literacy found that students lacked competencies in visual literacy and were not as visually literate as was assumed. When presented with images, the university students in the study lacked skills in critical analysis of visual components, including contextual details, possible manipulations, and what is being implicitly communicated. Therefore, competencies in visual literacy, media literacy, and other related literacies are of the utmost importance in teaching students. This essay will focus on the potential of a specific, emerging type of visual that I argue can be utilized for teaching visual literacy: the Internet meme.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".