Digital media, the developing brain and the interpretive plasticity of neuroplasticity
Bibliographic record
Abstract
The use and misuse of digital technologies among adolescents has been the focus of fiery debates among parents, educators, policy-makers and in the media. Recently, these debates have become shaped by emerging data from cognitive neuroscience on the development of the adolescent brain and cognition. "Neuroplasticity" has functioned as a powerful metaphor in arguments both for and against the pervasiveness of digital media cultures that increasingly characterize teenage life. In this paper, we propose that the debates concerning adolescents are the meeting point of two major social anxieties both of which are characterized by the threat of "abnormal" (social) behaviour: existing moral panics about adolescent behaviour in general and the growing alarm about intense, addictive, and widespread media consumption in modern societies. Neuroscience supports these fears but the same kinds of evidence are used to challenge these fears and reframe them in positive terms. Here, we analyze discourses about digital media, the Internet, and the adolescent brain in the scientific and lay literature. We argue that while the evidential basis is thin and ambiguous, it has immense social influence. We conclude by suggesting how we might move beyond the poles of neuro-alarmism and neuro-enthusiasm. By analyzing the neurological adolescent in the digital age as a socially extended mind, firstly, in the sense that adolescent cognition is distributed across the brain, body, and digital media tools and secondly, by viewing adolescent cognition as enabled and transformed by the institution of neuroscience, we aim to displace the normative terms of current debates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".