In Their Words: Exploring Language and Terminology Perspectives Among Individuals with Learning Disabilities
Bibliographic record
Abstract
There has been a long-standing debate between the use of person-first and identity-first language for individuals with disabilities. As such, we explored the perspectives of individuals with learning disabilities (LD) as to their preferences for these terminologies. We were also interested in examining their preferences for the term LD in general. One hundred twenty individuals were recruited online to share their perspectives. Overall, there does not appear to be a preference in terminology for LD individuals when it comes to person-first and identity-first language, and they have varying opinions as to why one options is better than another. Moreover, these individuals had different perspectives on the term LD, whether positive, negative, indifferent, or conflicted. Nevertheless, only a third of participants identified an alternative term for LD, with the most popular alternative being “learning difference,” followed by “neurodivergent.” The results of this research provide an important opportunity for individuals within the school including, teachers, school psychologists and administrators to consider the terminology utilized when talking about individuals with LD. In closing, we provide limitations and recommendations for future research.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".