MétaCan
Menu
Back to cohort
Record W2046934141 · doi:10.3138/cjccj.49.4.519

Offender Risk Assessment and Sentencing

2007· article· en· W2046934141 on OpenAlexaffvenueabout
James Bonta

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2007
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsPsychologyCriminal justiceRisk assessmentCriminologySuspectManagement

Abstract

fetched live from OpenAlex

I would like to thank the editors of this special issue of the Canadian Journal of Criminology and Criminal Justice examining risk assessment and sentencing for inviting me to comment on the three articles that form the basis of the issue. I was surprised that the editors asked someone who for years has collaborated with Don Andrews and is, therefore, likely to provide a commentary that may not be very much different than one Andrews would write. I did disclose to the editors my close working relationship with him, and I would like the readers to know of my bias. Nevertheless, the editors felt that I might have something substantive to contribute to the discussion and asked me to write this commentary. Differentiating offenders in terms of their risk to re-offend has been a major preoccupation of corrections ever since Burgess developed a simple, actuarial measure in 1928 to assess who is a good risk for parole and who is not. However, it was not until the 1970s that social scientists took the risk-assessment business seriously and began to develop objective assessment instruments that yielded predictive accuracies surpassing the judgements of psychiatrists, psychologists, and social workers. The most important advance in offender risk assessment came from the work of Don Andrews and his colleagues, first seen in the 1980s (Andrews 1982) and elaborated in the 1990s (Andrews, Bonta, and Hoge 1990; Andrews and Bonta 1994). This was the integration of dynamic risk factors (or criminogenic needs) with static risk factors in risk/need instruments such as the Level of Service Inventory--Revised (LSI-R; Andrews and Bonta 1995). The use of evidence-based risk/need-assessment instruments in corrections has exploded in the last decade. All but two Canadian provincial and territorial correctional systems use an empirically defensible offender risk/need instrument, and the remaining two jurisdictions (Alberta and Quebec) are in the process of implementing such instruments (a similar trend is seen in the United States and the United Kingdom as well as other countries around the world). The value of risk/need instruments is not limited to decisions around who should be supervised more closely or who should be kept in custody for the protection of the public. Because these instruments also sample criminogenic needs, they can be used to direct rehabilitation services in order to reduce offender risk. The value of objective risk/needs instruments is readily apparent to correctional agencies. The question that the three papers here raise is whether risk/needs instruments have a place in pre-sentencing decisions. Andrews and Dowden argue that there is value in the courts' considering risk/need assessments, whereas Maurutto, Hannah-Moffat, and Cole are much more wary of a role for these instruments in the sentencing process. An exercise in knowledge destruction Rational empiricists highly value and respect evidence. It is systematic, objective, replicable evidence that makes or breaks a theory. Without a strong respect for evidence, we are left with personal, ideological explanations of a phenomenon. It is difficult for people, and scientists, to give up on notions that they have cultivated for years, as empirical evidence to the contrary grows. Look at how we have dealt with the issue of climate change. Although the alarm bells were sounded more than 30 years ago, it was not until this year that a consensus report from scientists from around the world unequivocally concluded that human beings have had a hand in climate change. How does one remain committed to a viewpoint that is contrary to evidence? The answer is to engage in knowledge-destruction techniques (Andrews and Bonta 2006). That is, adopt only that knowledge that supports one's position and discard knowledge to the contrary. The Maurutto and Hannah-Moffat article questions the very validity of risk/needs instruments, thereby pre-empting consideration of whether there is a useful role for these instruments in the sentencing process. …

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.213
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.112
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0060.006
Scholarly communication0.0100.005
Open science0.0020.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.103
GPT teacher head0.360
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207