Taking the Long View on Digital Culture and Mental Health: Principles From Critical Realism
Bibliographic record
Abstract
There is a widespread concern that some aspects of digital media use may be detrimental to mental health. Digital media such as social media, video games, and artificial intelligence profoundly influence the daily lives of young people through practices embedded in global and particular digital cultures. Because digital technologies and their uptake are actively evolving in ways that cannot be fully anticipated, research must "take the long view" to develop robust theories that stand the test of time. The philosophical approach of critical realism, developed by Roy Bhaskar and others in the 1970s-1990s, provides helpful parameters for studying complex phenomena such as the relationship between digital cultures and mental health. Critical realism sees science as a social process that aims to uncover the generative structures of reality, with the understanding that causal mechanisms exist at multiple levels of inquiry, from the material to the social. Drawing from the work of Bhaskar and other critical realists, this article outlines three methodological guiding principles for the study of digital culture and mental health: (1) focusing on the causal effects of digital media, located in individual practices, technologies, sociocultural structures, or elsewhere; (2) combining methods (qualitative and quantitative) and disciplines; and (3) actively engaging with the social dimension of research. Research on digital media and mental health has the capacity to illuminate causal mechanisms and their possibilities for human emancipation, even when these mechanisms are not fully actualized or directly observed.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.122 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.014 |
| 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".