The Medium Becomes The Self: The Clinical Framework for Algorithmic Identity
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
Over the last decade, mental health hospitalizations among young people (particularly Generation Z and the emerging Generation Alpha, born after 2012) have surged, with growing evidence linking this rise to problematic smartphone, social media, and now AI relational or companion use. Canadian data shows significant increases in hospital admissions for eating disorders, self-harm, and anxiety during high-risk periods such as the COVID-19 pandemic (Roumeliotis et al., 2024). Concurrently, research implicates the structure of digital platforms themselves in exacerbating depression, anxiety, disordered eating, and identity disturbance in Gen Z; appearance-driven platforms like TikTok and Instagram intensify social comparison, FOMO, compulsive self-monitoring, and cyberbullying (Shehab et al., 2025). As Gen Z has aged within this crisis, while Gen Alpha enters the same crisis a decade later, rates of underemployment, debt, emotional dysregulation, and overall life dissatisfaction continue to rise. Despite this now well-documented developmental emergency, mainstream mental health care models have not meaningfully adapted. Cognitive Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), and standard psychiatric assessments continue to overlook digital behaviours, algorithmic feedback loops, and AI-mediated interactions, both for Gen Alpha currently in crisis and for Gen Z living in its aftermath. From a media-ecology perspective, smartphones and social media are not neutral tools but environmental forces that reshape cognition, social perception, emotional regulation, and identity formation. Persistent engagement with interactive, appearance-focused platforms emerge as the Fourth Person (Robertson, 2025): a digital identity layer that exists alongside, but psychologically separate from, the offline self. This fragmentation amplifies anxiety, compulsive posting, regret, low self-worth, and interpersonal instability, yet it remains absent from clinical assessment frameworks. Emerging evidence further shows that AI “cyber-companion” systems cause psychosis, and/or intensify identity fragmentation by reinforcing the emotional, perceptual, and cognitive needs of the Fourth Person. These dynamics reveal a profound gap in current mental health care: digital and AI-mediated behaviours are not lifestyle preferences but core mechanisms of contemporary psychopathology. This thesis proposes a comprehensive, multi-level adaptation of mental health care that systematically integrates smartphone, social media, and AI use into assessment, diagnosis, and therapeutic intervention. Without such integration, the system will continue failing the very populations most harmed by the environments they were raised in; Gen Z after the crisis, and Gen Alpha entering the same crisis in real time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 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