Invisible Lives: Using Autoethnography to Explore the Experiences of Academics Living with Attention Deficit Hyperactivity Disorder (ADHD)
Bibliographic record
Abstract
Despite some notable voices clamouring for a shift in attitudes (Brookfield, 2011, 2014, 2017; Procknow, 2017; Fernando, 2017), little appears to have changed in the world of adult education when it comes to mental disorders. Particularly notable is the absence of first–hand accounts by adult educators struggling with these conditions, in our case, ADHD. Although there is a growing body of literature in adult and higher education about the impact of Attention–Deficit/Hyperactivity Disorder (ADHD) on student achievement, there is very little research that explores how university faculty learn and think about mental disorders and learning disabilities. Accordingly, this self–study explores the dynamics associated with adult ADHD by identifying themes, issues and concerns associated with being a faculty member with ADHD in contemporary academic settings. Given the widespread nature of the lack of faculty knowledge about learning disabilities and ADHD we see this issue as one that is at its core a problem for adult learning, particularly within today's universities.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".