Postdigital Literacies in Everyday Life and Pedagogic Practices
Bibliographic record
Abstract
Abstract In this article, we contribute to literacy and education studies by proposing three overarching features (elements) of postdigital literacy events, informed by an ecological, relational, and sociomaterial framing of literacy. These features are: (1) entanglement, (2) digital materiality, and (3) spatiotemporality. Entanglement is about how learning entangles with our social, cultural, and material contexts through communicative acts, which include an understanding of media entanglements through interface seams(/seaminess) and transmediality. Digital materiality reminds us that digital media are also material, including notions of digital matter, instantiation, representation, and significance. The feature of spatiotemporality accounts for spatiotemporal flow and presence across online/offline environments, concerned with postdigital reconfigurations of time, space, and place. All three elements incorporate challenges around equity, power, and values. We also advance theory-in-practice by providing examples of how postdigital literacy features/elements manifest in pedagogy and everyday life, based on our experiences as academics working in diverse contexts. This work can support international research, teaching, and knowledge around how media practices interrelate through postdigital communication and literacy experiences, what that means, and with what effects , within a vision of education for mutually enriching planetary futures.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".