Quality of Life as a Paradigm With Multiple Applications in the Field of Intellectual and Developmental Disabilities
Bibliographic record
Abstract
ABSTRACT This paper sets out an argument for conceptualizing quality of life as a useful paradigm in the field of intellectual and developmental disabilities. In doing so, it addresses the core elements of a paradigm as it is generally understood in scientific circles. The ideological and theoretical roots of quality of life are centered around core values, are closely connected to social constructs that have developed over time, are currently widely accepted in our field, and are supported by numerous laws, policies, and conventions. The meaning of quality of life as a paradigm is explained. One core premise of a paradigm is that its ideological and theoretical base should be usefully applied in current social contexts. This paper argues that the overall objective of improving quality of life for people living in their own contexts may be pursued through numerous application frameworks, each of which may have numerous specific application strategies that are best suited to the specific “quality” objectives being sought within specific contexts. For this reason, exemplary application frameworks and more specific application strategies are presented, and a case is made for ensuring that their outcomes are both achievable and positive. This, then, constitutes a new and broader understanding of quality of life as more than a useful social construct or a system of measurement, but rather as the overall thrust behind everything we do in our field and that may be realized in multiple ways. A summary figure is provided.
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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 | 0.033 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".