Symposium 3: Tomorrow’s Networked Posthumans: Reflections on Artificial Intelligence and the Digital Well-Being of Young Children
Bibliographic record
Abstract
While networked learning (NL) is most often associated with adult learning and professional work practices, examining the “ontogenetic development” of children in the context of today’s smart global networks is also relevant to NL research (Rodríguez-Illera & Barberà in NLEC et al., 2021). In this paper, we ask: What child-technology relations are being forged in our posthuman era of Artificial Intelligence (AI), big data and global networks? We begin by scoping the intensifying presence of networked, smart technologies in the home life of infants, toddlers and preschoolers; we examine recent policy frameworks regarding AI, ethics and children. We then turn to two phenomenological philosophers, Michel Serres and Bernard Stiegler to consider how their thinking about digital technologies might provide insight for parents and educators as they endeavour to make the best “smart” technology choices for children. Finally, we consider the implications of our phenomenological reflections on today’s young posthumans for networked learning and postdigital 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 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".