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Record W2929839016 · doi:10.11114/ijlpa.v2i1.4117

Wrongful Convictions and the State Risk Harm Paradigm

2019· article· en· W2929839016 on OpenAlexaff
Myles Frederick McLellan

Bibliographic record

VenueInternational Journal of Law and Public Administration · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsAlgoma University
Fundersnot available
KeywordsGovernmentalityHarmContext (archaeology)NormativeCriminologyCharterRisk societyPolitical scienceLaw and economicsSociologyLawPoliticsSocial science

Abstract

fetched live from OpenAlex

What has been seen in the last thirty-five years is a significant shift in the psyche of contemporary society. Beck’s theory of “risk society” has captured the concerns of governments and its institutions to focus fears on risks and insecurity. Within the criminal justice context, this has led to the pervasive consciousness that crime has become part of the everyday experience to be controlled by risk management techniques framed within Foucault’s concept of “governmentality.” Crime has become a ubiquitous risk that must be routinely assessed and managed. This shift in criminological thought has also been seen in the move away from the liberal ideals of due process to the favoring of public protection over the rights of individuals found within the normative model of crime control. The problem in this devaluation of due process is the consequent imbalance of power between the individual and the State. Due process rights are enshrined in the Charter to protect against this imbalance and are never more important than when loss of liberty is at stake, most particularly when the errors due to the constriction of these rights contribute to the acknowledged systemic factors that lead to wrongful convictions.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.049
Scholarly communication0.0080.009
Open science0.0020.007
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0060.001

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 head0.336
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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