Toward an Obsessive-Compulsive Madtime
Bibliographic record
Abstract
Acute experiences of obsessive-compulsive distress both speed up time in the frantic repetition of the obsession and compulsion, and cause delays and slowed progression through the necessary activities of daily life. Both inwardly fast and outwardly slow, experiences of obsessions and compulsions, medicalized as obsessive-compulsive disorder (OCD), present a paradoxical temporality marked by repetition in an ongoing present in deferral of a feared future. Through first-person lived experience and literary analysis, this article considers the specific ways that obsessive-compulsive madtime is lived and rhetorically constructed. I consider my recollection of a period of intense obsessive-compulsive distress as a way to story the experience of mad temporality from a personal, situated location. I then look to how the narrator in John Green’s 2017 young adult novel Turtles All the Way Down rhetorically figures obsessive-compulsive madtime through sentence and paragraph structure in dialogue with her obsessive and compulsive thoughts. I argue that obsessive-compulsive madtime functions as a doubled perspective of self and logic, but also as a doubled perspective in temporality whereby repetition forestalls a future due to the inability to find a reassuring sense of memory and completion. Obsessive-compulsive madtime proves an informative place from which to think through the overlapping experience of madtime and normative, sane time, and being in anxious narrative tension with futurity in the present.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".