Bibliographic record
Abstract
Making Mentors project, autistic CSI students who major in science, technology, engineering, and mathematics (STEM) fields will be paired with autistic high school students on Staten Island ."When I first started at CSI in 2012, there was a call to develop programs for autistic students," says Gillespie-Lynch .CSI is a senior CUNY institution, offering associate and bachelor's degrees as well as several graduate programs .She explains that this makes CSI attractive to a number of autistic students because they don't have to transfer to progress in their educations .With a previous NSF grant, Gillespie-Lynch oversees a game design and employment workshop with autistic youth to help them develop employment skills .She says this new grant is in the same vein of using STEM (science, technology, engineering and mathematics) activities and autistic-led participatory work to empower autistic youth to acquire employment-related skills . CollaborationMartin is the principal investigator of Making Mentors and there are three co-principal investigators, one of whom is Gillespie-Lynch .Some occupational therapy doctoral students are also working on the project and are helping develop the focus on self-advocacy .The autistic college students at CSI have been identified through Project REACH and with the Office of Accessibility .Project REACH: Resources and Education on Autism is a CUNY-wide project created to enhance CUNY's capacity to serve the growing population of college students with autism spectrum disorders (ASD) .There are currently Project REACH programs at eight CUNY campuses .Each campus has unique elements, but a central point is mentorship .Training is provided for faculty and students ."It's about
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.014 |
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".