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Record W2100279836 · doi:10.1177/0306624x09336131

Measuring Hope

2009· article· en· W2100279836 on OpenAlex
Krystle Martin, Lana Stermac

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2009
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRecidivismConstruct (python library)PsychologyCriminologyRelation (database)PopulationRisk assessmentApplied psychologySocial psychologySociologyComputer securityComputer science

Abstract

fetched live from OpenAlex

In contrast to growing regard for the psychological construct of hope in medical and psychological arenas, hope has not yet found a permanent place in the field of criminology. Traditionally, treatment programs and risk assessment tools have focused on the deficits that criminal offenders possess. However, the orientation of our approach to corrections has recently shifted to embrace more strengths-based, offender-focused programming designed to rehabilitate offenders. The assessment for risk for recidivism has been slower to make this transition and use research supporting the use of more dynamic risk factors for reducing reoffending. This study investigates the nature of hope in offenders in relation to their risk for future criminal behaviour. The results indicate that hope is indeed related to the risk for reoffending. The information obtained through this research will inform researchers and clinicians about the nature of hope in a correctional population and its relation with risk for future criminal behaviour.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.365
GPT teacher head0.381
Teacher spread0.016 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it