ALGORITHMIC LITERACIES: IDENTIFYING EDUCATIONAL MODELS AND HEURISTICS FOR ENGAGING THE CHALLENGE OF ALGORITHMIC CULTURE
Bibliographic record
Abstract
Algorithms are interwoven in the fabric of digital culture. They increasingly mediate our experience of politics, culture, identity, and agency. Building on critical research in other fields, critical educational theorists are exploring the pervasive role of algorithms, AI, and ‘smart learning’ tools in reshaping what and how we learn. This work is articulating new critical literacies adequate to the challenges of ‘algorithmic culture’, where algorithms co-produce, with users, differentiated media experiences, knowledge, affinities, and communities, as well as new patterns of identity and embodied action. This article examines how educational theory is responding to the dramatic shifts in digital experience precipitated by algorithmic systems and explores how educators can support students in developing critical literacies and technical skills for navigating emerging algorithmically-mediated worlds. We offer conceptual and pedagogical heuristics to educational researchers and educators for navigating the challenges of algorithmic culture, as well as identify risks associated with the migration of big data techniques into formal educational spaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".