Rethinking enabling occupational performance: Can the self-driving car be our road map?
Bibliographic record
Abstract
Over the course of about 20 years of research, our lab had learned how to enable individuals, with all manner of performance problems, to experience performance success, ranging from the relatively mild problems seen in children with developmental coordination disorder to the severe problems seen in adults after a stroke or even in youth with dystonia. Using that metaphor allows us to understand that human occupational performance--as with the performance of the self-driving car--stems from two sources, hardware (the motor and sensory systems) and software (the neural networks). [...]the act of enabling human occupational performance must be one that includes the enablement of writing of neural code to support skill learning, and we, as occupational enablers, must become proficient in enabling our clients to write their own neural codes. At the risk of stretching the self-driving car metaphor too far, to enable our clients' occupational performance, we must develop expertise in (re)writing code to address bugs in their neural programs; we must become proficient at enabling them to (re)write their own code--that's what we did for Grace!
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".