Development and validation of the Good Lives Questionnaire
Bibliographic record
Abstract
The Good Lives Model (GLM) is a framework of rehabilitation when working with individuals who have committed criminal offenses. However, its core assumptions (i.e. that the ‘good life’ is comprised of various universal primary human goods) have not been tested, and there is no standardized measure of these concepts. We used a large community sample (N = 1,309) to develop a measure of primary human goods. Our 100-item draft Good Lives Questionnaire (GLQ) was reduced to 35 items via exploratory principal components analysis (n = 900), with its five-factor structure supported by confirmatory factor analysis (n = 409). This structure runs counter to the existing scholarship related to the GLM, which proposes eleven primary human goods. We found each of our factors – ‘Inner Peace’, ‘Energy and Agency’, ‘Social Connectedness’, ‘Varied Leisure Activities’, and ‘Spirituality’ – to be differentially associated with measures of self-reported aggression, criminality, and delinquency, supporting its validity as a measure of crime- and delinquency-related constructs. They were also associated with measures of psychological wellbeing, personal agency, social connectedness, and personality. We discuss the future validation of the GLQ, as well as its potential utility in clinical and forensic settings. An open access preprint of this paper is available at https://psyarxiv.com/5trj9.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".