Using the quality of life framework to operationalize and assess the <scp>CRPD</scp> articles and the Sustainable Development Goals
Bibliographic record
Abstract
Abstract This article describes how rights, the United Nations Sustainable Development Goals (SDGs), and the quality of life (QOL) framework are closely interrelated. Although legislation can be used as a tool for the practical application of QOL principles, QOL assessment information is required to further develop legislation and monitor the fulfillment of laws, policies, and the SDGs. A validated QOL model, which provides a set of concepts that can be one useful way for understanding and assessing QOL, can also function to assess many of the rights and goals promulgated in the Convention on the Rights of Persons with Disabilities (CRPD) and in the SDGs. This article illustrates the overlap between the CRPD, SDGs and QOL using the #Rights4MeToo Scale, a new measurement instrument for people with intellectual and developmental disabilities (IDD). The instrument's value lies in its potential to: (a) raise awareness about the rights enshrined in the CRPD; (b) design, implement, and evaluate the effectiveness of interventions aimed at facilitating the exercise of those rights and the achievement of the SDGs; and (c) ultimately improve the QOL of people with IDD.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".