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Record W3125675169

Le vieillissement de la population carcérale sous responsabilité fédérale au Canada: Vers de «pénitenciers-hospices» ?

2009· preprint· fr· W3125675169 on OpenAlexaboutno aff
Anne-Laure Tesseron

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonPopulationPrison populationCriminal justiceEconomic JusticeService (business)Population ageingPolitical scienceCriminologySociologyGerontologyDemographyLawMedicineEconomicsEconomy
DOInot available

Abstract

fetched live from OpenAlex

The ageing of the prison population under federal responsibility is a more and more obvious phenomenon in Canadian penitentiaries, as is the increase in the number of prisoners of 50 years and over. The stakes connected with these two phenomena are many and constitute major challenges for the correctional authorities in providing health services, in finding the necessary material and financial resources, and in reorganizing the penitentiaries. This report has as its objective, on one hand to analyze the process of ageing of the prison population and, on the other, to forecast the number of prisoners of 50 years and over. This will allow us to answer our main research question: are we moving towards "penitentiary-homes"? The data were supplied by the Correctional Service of Canada for 2001-2002 to 2006- 2007 and result from a vast data base which is constantly enriched: The Criminal Justice Information Library. From these data, several indicators were calculated. The mean and median ages of various classes of prisoners allowed us to estimate the prison ageing while the rates of flow in and out of the system make possible projections of the prison population. We found an increase of the mean and median ages of the prisoners condemned to long sentences, combined with a slight increase in the age of admittance. However both age and age at the time of admission were relatively stable for those with short sentences. These observations confirm what had been observed in the literature: the ageing of the prison population can be explained largely by the existing penal system and the increasing severity of legislated penalties. In a second step, the projections of population highlighted a net increase of the number of prisoners of 50 years and over from 2007 till 2017 if current practices persist. So, penitentiaries have to face not only an ageing of their population but also an increase in the number of older prisoners. These two phenomena will change inexorably the organization of the federal establishments even if older prisoners remain a minority. The correctional authorities will have to adjust to this new prison reality.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.372
Teacher spread0.345 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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