Swipe-Technology’s Influence in Born Digital Culture: Redesigning Pedagogy in Early Childhood Education
Bibliographic record
Abstract
This essay examines the ways in which technology defines and divides generations and considers how swipe-technology (touch-screen technologies) shape emerging learning styles. Specifically, it focuses on the research currently being investigated on how forms of digital literacy represent a radical shift, away from traditional forms of literacy (Prensky, 2001a, b; Frand, 2000; Prensky, 2001b; Tapscott, 1997; Franco, 2013; Plowman & McPake, 2013; Infante, 2014; Passey, 2014) and evaluates various claims made about the social consequences of such change. This paper emphasizes the impact that swipe-technology has on young children during early stages of their development and seeks to answer the following question: what are the consequences of digital language becoming the Born Digital’s (Franco, 2013) primary form of expression? The paper draws on some traditional theories such as those of Mannheim (Kecskemeti, 1952) and Vygotsky (1929, 1962, 1978) to provide a broader contextualization. In so doing, it hopes to contribute to the dialogue about how educational institutions should be redesigned to accommodate new media technologies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".