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Probation: pros and cons

2023· article· en· W4386824360 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMan crime and punishment · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsConvictRecidivismSocializationLawWork (physics)Quarter (Canadian coin)Service (business)Russian federationInstitutionResocializationPolitical sciencePrisonCriminologyPublic relationsSociologyPsychologyBusinessSocial psychologyEngineeringHistory

Abstract

fetched live from OpenAlex

As a result of the conducted research, it was found that the measures taken in the Russian Federation for the purpose of adaptation and re-socialization of former convicts are insufficient for the following reasons. Firstly, the psychological and educational work carried out with convicts is ineffective due to the lack of trust among the latter in the staff of the psychological service. Secondly, the specialties that a convict can master in a correctional institution are not relevant. Former convicts, being livestock breeders, turners and seamstresses, cannot represent a competitive force in the labor market. Thirdly, about a quarter of all crimes are committed by previously convicted persons. These facts indicate that the adoption of the Federal Law "On Probation in the Russian Federation" is a timely measure that can prevent further recidivism of crimes and adapt former convicts to life in society. However, the text of this law excludes the concept of "pre-penitentiary probation". However, it is necessary because: 1) a small number of citizens can afford to conclude an agreement with a lawyer due to the high cost of his services; 2) courts, choosing a measure of restraint on particularly serious articles, most often decide to detain the accused, while the accused cannot fully realize their legitimate interests and build a line of defense together with their lawyers.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.233

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 head0.050
GPT teacher head0.367
Teacher spread0.317 · 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