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Record W4366183186 · doi:10.4324/9781003421719-2

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2023· book-chapter· en· W4366183186 on OpenAlexaboutno aff
Don MacNiven

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessComputer science

Abstract

fetched live from OpenAlex

The Royal Canadian Mounted Police (RCMP) have been trying for a long time to identify and locate the mastermind behind a criminal ring that recruits teenagers into drug peddling and prostitution. The criminal has adopted an assumed identity and as a result is extremely difficult to locate and so more dangerous. The RCMP finally have one lead. They learn from an undercover police agent that the drug king-pin sprained his ankle sometime in August 1977 and had it attended to in a small town Ontario hospital. They go to the appropriate Ontario Health Insurance Plan (OHIP) official and ask to see the patient treatment records for the month of August. The OHIP official faces a classic moral dilemma, a problem which puts us into a no-win situation. The OHIP case could be seen as a problem of police ethics as well as a problem in medical ethics.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.316
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.003
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3160.095

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 head0.081
GPT teacher head0.326
Teacher spread0.245 · 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.

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
Published2023
Admission routes1
Has abstractyes

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