International Employment Statistics for People With Intellectual Disability—The Case for Common Metrics
Bibliographic record
Abstract
Abstract The World Report on Disability identifies employment as an important element of social participation. The Report also points to the need for research that crosses national boundaries to identify and address central areas of concern. However, such efforts are hampered with respect to intellectual disability (ID) by inconsistencies in the population definitions used, the definition of employment or employability, and metrics of employment participation. The authors explore the varied ways in which employment participation rates for people with ID are determined and reported in jurisdictions around the world, and note that with respect to employment metrics, there remains substantial variation in the methods used in data collection and reporting across agencies and countries. They also note that close inspection of methodologies is required in order to interpret data from any official statistical agency (as even when methods and definitions are explicit, the variations in approaches make comparisons difficult). Recommendations for harmonizing disparate definitions and metrics include a systematic analysis of the terminology and methods used in national surveys that would assist in identifying which data are comparable, agreement on a protocol and process for examining employment outcomes in the ID population, and the creation of an international panel on employment and ID charged to identify common terminology and population parameters to be specified in sample selection and description in localized research and studies.
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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 | 0.081 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.048 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".