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Record W2909644863 · doi:10.22215/etd/2018-13354

Observing Youth Punishment in the Social Systems of Law and Education

2018· dissertation· en· W2909644863 on OpenAlexaffabout
Kyle N. P. Coady

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsCarleton University
Fundersnot available
KeywordsPunishment (psychology)CriminologySociology of punishmentScholarshipSociologyContext (archaeology)LawPolitical scienceCriminal lawPsychologySocial psychology

Abstract

fetched live from OpenAlex

This dissertation explores youth punishment in Canada in the social systems of law and education.My research contributes to work in the sociologies of youth, punishment, education, and law, youth justice, and social systems theory.The present study is guided by a concern that there is a need for a meaningful account of youth punishment in social systems.It specifically focuses on three problems in the sociological study of youth punishment: a gloomy state of theorizing; the absence of a more frontally distinct analysis of youth punishment beyond the realm of exclusion; and the underdevelopment of inter-systemic and intra-systemic features of youth punishment.This study confronts these issues with an exploratory qualitative analysis of how punishment operates in the social systems of law and education.The questions guiding this work include: 1) how are youth punished in the systems of law and education, and 2) in the context of youth punishment in social systems, are the social systems of law and education linked, influenced or coupled, and if so, how?My project is different from other scholarship in the field as it relies on insights from Niklas Luhmann's contemporary social systems theory.This theory argues that social systems operate communicationally and the world is made up of functionally differentiated systems.This framework points me to study the legal and educational communications of punishment.This perspective foregrounds the inter-systemic and intra-systemic features of youth punishment in law and education.While many important contributions focus on the effects of punishment (exclusion, harshness, inequality…) and what (over)determines punishment (race, culture, morality, fears, politics…), my work addresses the absence of an empirical and theoretical understanding of youth punishment from the point of view of social systems.My research highlights the peculiarities of punishment in the social systems of law and education, and shows how punishment can connect social systems.I expose how there are distinctly educational and distinctly legal features of youth punishment.First, I present a suite of intra-systemic features of punishment in law.The peculiarities of youth punishment in law are captured with law's focus on offering protection, distinguishing fools from fiends, observing the character and associated consequences of youth behaviour, and pursuing accountability.Second, I show the peculiarities of youth punishment in education by documenting education's focus on the locality of behaviour, school climate, and progressive discipline.Finally, I analyse how education and law are able to influence each other, which means that law can productively make use of education and education can productively make use of law.My study provides the opportunity to be cognizant of the day-to-day workings of legal and educational punishment and the interactions between these two systems.This research shows that more attention could be paid to both the peculiarities of different social systems where punishment unfolds and the connections between social systems when punishing.

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)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.008
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.416
Teacher spread0.327 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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