Bibliographic record
Abstract
This paper explores from a critical mad studies perspective, the under-investigated relationship between madness and constructions of childhood. Through analyzing a video published by the Oprah Winfrey Network on YouTube where the host conducts an ‘interview’ with a young girl labelled with ‘childhood schizophrenia’ named Jani, I argue mad experiences of children are doubly characterized as dubious. Beyond dubious, they are conceptualized as a form of human experience so unfamiliar to sane adult perceptions they become conceptualized as entertainment. Moreover, I contend viewing the different psychogeographies or world(s) of mad people, more specifically children, that are not omnipresent to sane and mad minds alike, as farcical or disordered is problematic as it excises these realities are in fact real to those who experience them. This paper asserts the sanist move to only consider the shared phenomenological conscious experiences of populations at large is a form of cognitive injustice that ignores the multiplicity of realities that exist. What I propose towards the end of the paper is that scholarly analysis consider individuals as individuals, rather than what their subjective intersectional identities culturally demarcate. What mad positive futures can be imagined if we dare to consider Jani as Jani and not a ‘crazy little girl?
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".