Definitions Matter in Understanding Social Inclusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Social inclusion is an explicit goal of legislation, policies, and supports for persons with intellectual and developmental disabilities in many countries. However, evidence outlining the dimensions of social inclusion is still limited. How we understand social inclusion defines how it is measured. This study aims to better understand the concept and indicators of social inclusion. Retrospective analyses were conducted on 1,341 adults with intellectual disabilities residing in institutional and community‐based settings who were assessed with the “interRAI Intellectual Disability” instrument. Objective and subjective items in the instrument related to five domains of social inclusion (i.e., relationships, leisure, productive activities, accommodations, and informal support). The results highlighted the heterogeneity within domains, and by the nature of the indicator. Overall, percentages varied between 3.0% and 96.4% depending on which indicator was used; variability also existed in rates achieved using objective and subjective indicators. Acceptable‐to‐good levels of internal consistency were reported for three of the five domains; low correlations were found to exist between some, but not all, domains. The results of this study demonstrate that without an understanding of what social inclusion means for both general and vulnerable populations, it is not clear what is being measured, or how it should be measured. A clear definition of inclusion and its measurement is needed for decision‐makers and service providers to define the nature of their responsibilities, set actions, and assess their effectiveness in achieving inclusion.
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.
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 | 0.001 | 0.042 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it