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Record W4388718654 · doi:10.1177/20662203231212600

Leadership in Canadian and French high security prisons expectations and perceptions

2023· article· en· W4388718654 on OpenAlexaffabout
Martine Herzog-Evans, Rosemary Ricciardelli, Jérôme Thomas

Bibliographic record

VenueEuropean Journal of Probation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPrisonAutonomyCompetence (human resources)LegitimacyManagerialismSociologyTransactional analysisDignityProcedural justiceTransactional leadershipPublic relationsPerceptionSocial psychologyPsychologyCriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The literature has yet to study the ideal type of prison leadership in the eyes of frontline staff. Methodologically, most studies draw upon written questionnaires distributed to governors, not to their staff and analyse their data without using management or human needs theories. This study contrasts with the extant literature inasmuch as it is comparative (France/Canada) and draws upon Appreciative Inquiry interviews. It also draws upon three strands of literature and their variables: the general theory of management, self-determination theory and legitimacy of justice-procedural justice theories. The study finds that French and Canadian prison officers have needs in areas uncovered in this literature: competence, relatedness, autonomy, general fairness, respect/dignity and care. Our samples have mixed feelings about their local hierarchy; they are very critical regarding their national hierarchies. Prison officers describe their managers as being essentially either laissez-faire or as transactional. Limitations and institutional policy implications are explored.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.002
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 head0.065
GPT teacher head0.295
Teacher spread0.230 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Journal of ProbationSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207