Working With Uniqueness: Optimizing Vocational Strengths for People With Tourette Syndrome and Co-Morbidities
Bibliographic record
Abstract
This paper shares discoveries from a grounded theory inspired study of “optimizing vocational strength's and the unique attributes of Tourette Syndrome and co-morbidities, including Obsessive-Compulsive Disorder and Attention Deficit Hyperactivity Disorder (or TS/TS+), in the workplace. This one year study featured 16 participants with various levels of workplace functioning and health status and was set in an art college. Data gathering methods included individual/group interviews alongside observations of, and products from, studio art workshops in drawing, sculpture, performance, and creative writing. Data collected in this way elicited a breadth and depth of representation and harnessed the uniqueness and imagination of participants pivotal to recovery and supporting vocational optimization. The process of “optimizing vocational strengths” is revealed both visually and textually in this paper and is instructive for educational and vocational supports for people with TS/TS+.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".