Bibliographic record
Abstract
Ruth starts with details from her childhood, when she tended to be a perfectionist and had obsessions around contamination. Ruth suffered with episodes of depression herself, but they were short lived and didn’t require treatment. Her severe depression started while on a long travel in two continents. She developed delusions of guilt and was receiving messages, resulting in an admission to a hospital in Canada, and then permitted to fly back home under sedation. Several years later, after severe social stressors, she relapsed and remained depressed for several years. She was again psychotic, believing that she had ‘killed the world’ and eventually became almost mute. Her sister, aware that their grandfather had received ECT, researched the topic and felt that it should be tried, especially after listening to a talk by Dr Sherwin Nuland, who had ECT himself. It was him who used the phrase ‘rising like a phoenix’, which was chosen as the title of this chapter. Ruth had twenty-three sessions before she felt better. She describes in detail her memory problems. She has since followed Sherwin Nuland’s lead by talking about her ECT experience publicly.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.077 | 0.023 |
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".