MM:6 Wake‐up call: everything you always wanted to know about working with people with disabilities but were afraid to ask
Bibliographic record
Abstract
‘Wake‐up call’ was developed as a training DVD for staff that work with people with disabilities (known as ‘consumers’) and addresses the way that consumers want staff to speak, work, relate, and interact with them. It's all about attitude and respect. It is the result of a year‐long project that began when Consumer Self‐Advocacy groups at four UCP/NYC Day Habilitation programs around the city met in November 2006 to discuss the expectations of people with disabilities. The resulting 12‐minute edited film will be used for training new UCP/NYC staff and will be available for distribution around the country and Canada. ‘Wake‐up call’ features Geri Jewell (HBO's Deadwood), who narrates and performs, and Commissioner Matthew Sapolin of the Mayor's Office for People with Disabilities in New York City. Sapolin, who is blind, contributed his own thoughts, including his reluctance to be considered an ‘inspiration’ or ‘hero’. As Jewell comments on screen, ‘everyone in this video has one thing in common – they are people like me and you’. Wake‐up call also features the song ‘I Need To Wake UP’ by award‐winning singer/songwriter Melissa Etheridge. The film was co‐produced by UCP/NYC staff Scott Feldman, Director of Waterside residences, and Lois Silver Fiala, Media Manager. Silver Fiala, who has won several awards for previous films, found the non‐scripted format to be extremely enlightening. ‘After working with consumers for over 20 years, it was fascinating for me to gain even further insight into what they have to say’.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.479 | 0.143 |
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".