Learning from the information workspace of an information professional with dyslexia and ADHD
Bibliographic record
Abstract
Dyslexia is one of the least understood so-called ¿learning disabilities¿ (LD), characterizing problems with text, organization, working memory, attention, and mental sequencing. Perhaps surprisingly, other research shows that dyslexia and LD may include strengths. For example, astronomers identified with dyslexia were able to spot patterns in imagery that were less visible to their non-dyslexic counterparts. The purpose of this investigation was to describe the information practices and spaces used by an information professional identified as dyslexic, focusing on their successful strategies within an office workspace context. The participant 1) made extensive use of non-traditional workspaces that contained or had access to specific kinds of ¿noise¿ that served as ¿intentional distractions¿ to paradoxically increase focus, 2) made extensive use of ad doc organizational ¿clutter¿ on horizontal surfaces, and 3) extended their ad hoc organization into their computer workspace by relying more on search queries rather than hierarchical file systems. The paper concludes with recommendations for new kinds of information systems that may assist office workers who share this particular cognitive phenotype.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".