Symposium 3: Tomorrow’s Networked Posthumans: Reflections on Artificial Intelligence and the Digital Well-Being of Young Children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it