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Record W1486420913 · doi:10.25959/23210759

Ageing Prisoners - Significant Cohort or Forgotten Minority?

2006· dissertation· en· W1486420913 on OpenAlexaboutno aff
DJ Heckenberg

Bibliographic record

VenueeCite Digital Repository (University of Tasmania) · 2006
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonEthnic groupDemographicsPopulation ageingCriminologyContext (archaeology)AgeingOlder peopleLife course approachDeviance (statistics)SociologyPopulationPolitical scienceGerontologyPsychologyMedicineGeographySocial psychologyDemographyLaw

Abstract

fetched live from OpenAlex

In 2006 rising numbers of older offenders represent a significant strategic issue for prisons in the United Kingdom, Canada and the United States. A statistical analysis of the number of older prisoners in Australia reveals similar trends. For the purpose of this research 'ageing prisoner' means a man or women aged 45 or above. This thesis seeks to contribute to an understanding of what it means to age in prison, and explores ageing in the context of population demographics, Aboriginality, ethnicity, social class, gender, deviance, and the 'positive ageing' concepts that inform twenty-first century discourse. A discussion on the concept of 'ageing in place' in the prison environment draws on national and international literature to identify the experiences of older prisoners, and highlight the emerging challenges for prison administrators. This thesis involved an extensive analysis of prison statistics from Tasmania, Victoria, South Australia and New Zealand. It provides an extended discussion of the dynamics and challenges of ageing in prison and concludes with a synopsis of the issues confronting service provision and prison processes in the light of the offence profiles and special needs of older inmates.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.237
Teacher spread0.225 · 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 designQualitative
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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueeCite Digital Repository (University of Tasmania)Same topicCriminal Justice and Corrections AnalysisFrench-language works237,207