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Record W3206610530 · doi:10.32920/ryerson.14653452.v1

Hillsborough : Truth And Trauma

2021· preprint· en· W3206610530 on OpenAlexaff
Lynne M. Fox

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInquestBrotherStadiumFootballHistoryLawCriminologyPoliticsSociologyPolitical science

Abstract

fetched live from OpenAlex

On April 15th, 1989, at the F.A. (Football Association) Cup semi-final football match, Hillsborough Stadium in Sheffield (U.K.), ninety-six men, women and children were crushed to death. Now known as the Hillsborough Disaster, this event is the worst sporting disaster in the history of England. My twenty-one year old brother, Thomas Steven Fox, was one of those ninety-six victims. New inquests into the deaths of the victims of the Hillsborough Disaster opened in 2014. My documentary film, “Our Steve” is a forensic look at the inquest into my brother’s death in an attempt to document this process as it was happening. As the new inquests into Hillsborough re-entered the public sphere through media, television and political platforms, my need to represent this story and that of my family became the impetus for this project

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)0.001
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.225
Threshold uncertainty score0.448

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.009
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0710.008

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 head0.104
GPT teacher head0.448
Teacher spread0.345 · 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
Published2021
Admission routes1
Has abstractyes

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