Functional Measurement of Special Education Teachers’ and Students’ Expectations Toward Job Training for Persons with Intellectual Disability
Bibliographic record
Abstract
Persons with intellectual disability (PWID) have fewer opportunities for enrolment in school programs and post-school employment than do their peers with typical development. Evidence suggests that attitude toward PWID is a main factor in either promoting or limiting better life conditions for this population. In this paper, the goal was to determine the cognitive information integration rules underlying the expectations of 174 special education teachers and students with regard to job training for PWID. In order to accomplish this goal, four factors (Gender, Severity of disability, Type of task, and Emotional traits) were orthogonally combined to implement a cognitive algebra study design. We obtained 48 experimental conditions, with each one presented as a scenario describing a PWID in a work training situation. Participants read these scenarios and were asked to judge the probability of the success of PWID with regard to learning the skills needed to complete the required work. Patterns of response allowed us to identify low, moderate, and high viewpoints with regard to participants’ judgments of predicted success. Personal factors (Emotional traits and Severity of disability) and the Type of task factor were considered the most important in influencing the participants’ judgment. These factors seemed to be integrated in a complex systematic cognitive pattern. Implications from this type of result with regard to PWID and work training are discussed in this paper.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".