Manufacturing a Monster: an autoethnographic analysis of enforced isolation, objectification and the destruction of self
Bibliographic record
Abstract
Introduction: This perspective article examines the impact of enforced isolation on the authors sense of Self. The research explores how systemic objectification and the blocking of vital "mirroring" within seclusion and long-term segregation (LTS) in psychiatric hospitals in England can lead to the erosion of Self. The paper posits that enforced isolation is not therapeutic, but a destructive intervention rooted in neuronormative ideology that ultimately escalates distress, prolonging detention. Methods: The autoethnographic perspective offers a qualitative understanding of experience to examine the phenomena of isolation and trauma. The reflexive analysis is rooted in the author's lived experiences of repeated and enduring exposure to seclusion and LTS. Results: Enforced isolation eroded the author's Self due to systemic objectification and a lack of positive "mirroring". Consequently, the Self could only be sustained through perverse connections with staff e.g., shared negative emotions such as fear, aggression and hate. With a Self-reconfigured around negative affect, the lines are blurred between intimacy and aggression, resulting in shattering implications for the author's ability to have relationships and love. Discussion: Enforced isolation is positioned as a destructive intervention, manufacturing rather than containing, distress. This perspective reframes isolation from a clinical tool to a harmful practice, contradicting therapeutic goals. Aligning with wider research, the paper calls for a transformative shift towards rights-based, relational models, such as HOPE(S), that prioritize human connection to prevent iatrogenic harm.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".