The design of the “autistics in (educational) space: building our own futures” doctoral project
Bibliographic record
Abstract
As an autistic researcher and doctoral candidate, I have designed my dissertation research in a way that values the lived experience of my four autistic participants. Using their responses to a series of material objects and a science fiction novel by, and about, an autistic person, I hope to find new and innovative ways to reconceptualize inclusive education in high school. This article explains my theoretical framework and methodology, as well as some preliminary results and discussion.Lay AbstractI am autistic and a PhD student and I look for ways to learn from other autistic people. I gave a group of 4 autistic participants 14 items and asked them to do something with each of them, then send me pictures of what they did. We also all read a science fiction novel written by an autistic author and talked about what we thought was interesting or that felt familiar to us. By using what they shared with me, I want to find ways to make high school more comfortable for autistic students. In this article, I describe how I came up with this plan, what I did, and some of the first things I discovered.
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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".