Bibliographic record
Abstract
Because of the COVID-19 pandemic, we are all now living in a world of mass panic, confusion, and isolation that inflicts experiences of mental illness on those not typically considered mentally ill. When, where, and how does identifying “mental illness” come to trap certain people under the stigmatizing identity, while others are able to avoid the problematic medical classification but not the lived experience? As a writer mitigating a long-term struggle between my lived experiences with depression and anxiety, and the outside categorization and medical classification of these “mental illnesses”, I realize the current public sentiment has never been more welcoming of my personal musings on these tensions. I have centered an autoethnographic approach that reflects on mental health experiences and critiques of biomedical ontologies through a reading of My Brilliant Friend (and the associated quadrilogy). By attending to socially relevant story arcs involving mental health, I use the symbol of book character Lila’s “blurred boundaries” to both identify and rethink mental health categorizations and lived experiences that previously differentiated subsets of people prior to COVID-19. My reflection ultimately seeks to address the ways that these once dissimilar groups have converged psychologically through disruptions of time during the current health pandemic.
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.
How this classification was reachedexpand
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 | 0.012 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".