Introducing Offlineness: Theorizing (Digital) Literacy Engagements
Bibliographic record
Abstract
In this Insights essay, we propose a new concept of offlineness that builds on current language around digital practices, yet addresses an element of young people’s experience that is not adequately represented in current research or educational discourse. This work is informed by a recent cross-national arts-based research project that highlighted the limitations of the discourse ascribed to the nature of young people’s engagement with digital literacies. We propose a (re)theorization, which builds on a critical review of current conceptual research and digital commentaries. Theorizing offlineness as a continuum between online and offline practices is tantamount to a paradigm shift toward more nuanced understandings of young people’s digital practices. It offers researchers and educators a more precise way to speak to young people’s digital experiences, providing a productive tool to (re)construct learning and inquiry spaces in literacy research and education.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.016 | 0.035 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".