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Record W2169115768 · doi:10.1177/0093854812449217

Incentives for Offender Research Participation Are Both Ethical and Practical

2012· article· en· W2169115768 on OpenAlexaff
R. Karl Hanson, Elizabeth J. Letourneau, Mark E. Olver, Robin Wilson, Michael H. Miner

Bibliographic record

VenueCriminal Justice and Behavior · 2012
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcMaster UniversityUniversity of SaskatchewanPublic Safety Canada
Fundersnot available
KeywordsIncentiveDeterrence (psychology)Punishment (psychology)PopulationConsistency (knowledge bases)Public economicsDeterrence theoryCriminologyBusinessPsychologyPolitical scienceSocial psychologyEconomicsLawMedicineMicroeconomicsEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

There is little consistency in policies concerning incentives for offenders to participate in research. With nonoffenders, incentives are routine; in contrast, many jurisdictions and granting agencies prohibit offenders from receiving any external benefits. The reasons for this prohibition are unclear. Consequently, the authors reviewed the ethical and practical concerns with providing incentives to offenders. They conclude that there are no ethical principles that would justify categorically denying incentives for offenders. Research with offenders, however, presents unique practical concerns that need to be considered when determining the magnitude and form of the incentives. In general, the incentives should not be so large as to compel participation of a vulnerable population or to undermine the goals of punishment and deterrence. The authors propose that incentives for offenders should be routinely permitted, provided that they are no larger than the rewards typically available for other socially valued activities (e.g., inmate pay, minimum wage).

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 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)0.005
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.841
GPT teacher head0.700
Teacher spread0.141 · 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 teacher head, not a consensus.

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

Citations29
Published2012
Admission routes1
Has abstractyes

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