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Record W4389676890 · doi:10.1515/9780889775558-003

Foreword

2018· book-chapter· en· W4389676890 on OpenAlexaboutno aff
Gary Garrison

Bibliographic record

VenueUniversity of Regina Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

In my previous book, Human on the Inside, I talked about the nature of systems, how human beings need systems to frame how we live and interact and how at the same time these systems dehumanize us, turning us into mere customers, clients, inmates, files, bar codes, or pixels on a screen.In that book, my focus was the prison system in Canada, but all systems-whether it's marriage, religion, criminal justice, income tax, education, health care, you name it-embody the same principle.For us to be safe and healthy, whether as a community or as individuals, we need to find a balance between connecting with each other as persons and relying on systems that treat us as objects.This person-object tension is central to the life of everyone who cares for children who have to live separate from their parents, and, of course, to the lives of the children themselves.When a baby is born to an abusive mother or an addicted or runaway father-to pick one examplewho cares for the baby?The law says that if I know a child is being abused, I'm obligated to report it.But if I'm the grandparent, and reporting the abuse means a

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 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 score0.863
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.250
Teacher spread0.209 · 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.

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
Published2018
Admission routes1
Has abstractyes

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