Affective (Dis)Ability: Ian Brown’s Search for “Inner Life” in The Boy in the Moon
Bibliographic record
Abstract
This essay examines a father’s quest to find proof of the “inner life” of his physically and cognitively disabled son in Ian Brown’s memoir The Boy in the Moon: A Father’s Journey to Understand His Extraordinary Son. Through literary analysis and close attention to relevant theories of affect and disability, this paper explores the influence of dominant social and cultural narratives about normalcy and emotion on understandings of disabled lives as well as the limitations of current theories when it comes to recognizing the affective potential of those who fall outside what is considered the zone of normal physical, mental, and emotional experience and expression. I argue here that Brown’s quest to understand his son’s affectivity leads him toward a greater recognition of possibilities for human relationship beyond the intellectual or verbal. I find that the trajectory of Brown’s personal quest has important repercussions for the ways that theories of affect and disability studies can be productively brought together to formulate understandings of intersubjective and interdependent affective relationships for people with cognitive disabilities. Keywords: disability, affect, emotion, memoir, Canada
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".