Imaging, Keyboarding, and Posting Identities: Young People and New Media Technologies
Bibliographic record
Abstract
Part of the Volume on Youth, Identity, and Digital Media Clicking, posting, and text messaging their way through a shifting digital landscape, young people are bending and blending genres, incorporating old ideas, activities, and images into new bricolages, changing the face, if not the substance, of social interaction and altering how they see themselves and each other. From data collected in Britain, Canada, and South Africa, we have selected cases that involve a range of technologies and contexts, from adult-mediated activities in schools and community centers to spontaneous media production done in private at home. Whether it be postings on websites, improvisations in video production, or the incorporation of objects in a multi-media presentation, these cases illustrate that, like digital cultural production, identity processes are multifaceted and in flux, constructed and deconstructed through a process of bricolage that we label as "identities-in-action." Analysis of the cases reveals certain shared features of digital production that contribute to identities-in-action: the "constructedness" of production, the collective and social aspects of individual productions, the neglected but crucial element of embodiment, the reflexivity and negotiation involved in producing and consuming one's own images, the creativity in media convergence, and the value of constructivist models of learning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".