Bibliographic record
Abstract
In this series of podcasts, Kerrianne Morrison, Noah Sasson, Sue Fletcher-Watson, Catherine Crompton and Damian Milton and myself discuss our respective experimental research on the phenomenon of "double-empathy" - bridging the gap in understanding between autistic and non-autistic people. This podcast series comes from two recording sessions. The first was when myself, Kerrianne, Noah, Sue and Catherine met up at a large international conference on autism in Montreal 2019 (called INSAR in case you hear us referring to this). The second episode was recorded in London 2019 with myself and Damian, after Damian had had a chance to listen to the first recording and provide his reflections. A small note: I had some difficulty with sound interference during the recording and I have done my best to minimise this. My apologies in advance for any interference heard. I have animated this podcast as part of my wider project to create more diverse and accessible ways of engaging with knowledge. Soundcloud link to Episode 1: https://soundcloud.com/drbrett/double... Soundcloud link to Episode 2: https://soundcloud.com/drbrett/double... Links to research: To find out more about Double Empathy, follow Damian's work here: https://www.kent.ac.uk/social-policy-... To read more about our virtual symposium: https://dart.ed.ac.uk/insar-2019-virt... Kerrianne's and Noah's research mentioned: https://journals.sagepub.com/doi/abs/... Brett's research mentioned: https://www.frontiersin.org/articles/... Catherine and Sue's research mentioned: https://dart.ed.ac.uk/research/nd-iq/
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.363 | 0.116 |
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".