From theory to conceptualization, through operationalization: Comparing indicators of desistance from crime
Bibliographic record
Abstract
Appreciating the development of the literature on desistance and producing scientific research in the context of conceptual and operational instability is a complex exercise. Several constraints affect the choice of operationalization, including the characteristics of available data. Beyond the search for a perfect or consensual measure, it becomes imperative to understand how the definitional and conceptual choices shape and limit studies. The current analysis contributes to this literature by comparing three operationalization strategies for desistance from crime in quantitative and longitudinal designs: a binary measure of participation in delinquency, a scale measure of the versatility of offending, and a scale measure of the intensity of offending (original measure combining versatility and frequency of offending). Taxonomies of multilevel models for change were conducted with a subsample of data collected as part of the Montreal Two Samples Four Generations Cross-sectional and Longitudinal Studies to predict desistance as a function of age and various sets of common time-varying (level 1) and time-stable (level 2) independent variables. Results show differences in the capacity of every measure to capture desistance, and predictors vary in nature and number according to the predicted outcome (participation, versatility or intensity). Overall, results tend to indicate that the various measures are complementary; they provide a more complete picture of desistance as they capture different aspects or phases of the phenomenon. The strengths and limitations of each operationalization strategy are discussed. Relevant direction for further research considering the conceptual and operational diversity in this field of study are suggested.
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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 | 0.077 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".