Bibliographic record
Abstract
This chapter addresses the collaborative participatory nature of online interactivity within the range of social networking spaces afforded by Web 2.0 (O’Reilly, 2005). Each individual, through his or her situated usage patterns and choices, creates a unique digital fingerprint or electronic biography. Using a multiple case study method including children and youth ranging in age from five to fifteen years of age, the authors examined children’s online interactivity through their electronic biographies. This case report focuses on the children’s experiences of online interaction as a seamless component of their literacy (Thomas, 2007) and presents a profile of each young person that characterizes his or her unique online fingerprint. The findings provide insight into how children learn online interactivity, and their communities of practice at different stages of development. Their roles ranged from passive surfer-viewer-seekers to interactive discussant-displayer-players. Infrequently, some youth showed proactive leadership as host-builder-creators. The experiences of these young people provide practical evidence of the transformation of literacy; for them, the Internet serves as an information resource, a collaborative medium, and a design environment (Lapadat, Atkinson, & Brown, 2009). Narrative plays a key role online, especially in the construction of identity. The results of this study have implications for educators, parents, social scientists, and policy makers, and in particular, raise concerns about the commodification of childhood and how commercial interests have shaped sites used by children.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.025 |
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".