Bibliographic record
Abstract
This article reports on a 2016 pilot of a video-based technology mentoring program undertaken with young Australians with high functioning autism who are socially and geographically isolated. Many young people with autism live with deep social isolation due to their difficulties in mixing easily with others; this is further exacerbated for those living in geographically remote areas. These young people are subject to acute forms of exclusion, yet have long tended to be highly adept at the use of technology. The Online Lab is based on The Lab, a national network of face-to-face technology and social clubs for young people with high functioning autism. The pilot involved 25 remote or regional young people from three states. Synchronous weekly online sessions were led by expert mentors, with up to six young people participating via the Zoom video conferencing platform.
 The evaluation combined qualitative methods that could be administered remotely with local methods and de-identified usage statistics. It drew on the notion of ‘differentiated spaces’ as devised by Lye Ee Ng in her doctoral work at The Lab, which recognises that online, offline and personal spaces interact to facilitate different forms of social interaction. The evaluation concluded that the pilot was a ‘qualified success’; The Online-only format working effectively for some types of participants with high functioning autism, but less so for others. A number of related findings and recommendations will be outlined in this article. This article will also outline how these findings have been incorporated into the rollout of The Online Lab by The Lab organisation in 2017 and 2018, and how the program’s subsequent activities are, in turn, feeding back into The Lab’s understanding of how to effectively use technology for mitigating social isolation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".