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Record W2891743775

Good times for free

2018· article· en· W2891743775 on OpenAlexaboutno aff
William Lakin

Bibliographic record

VenueMiddlesex University Research Repository (Middlesex University Of London) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionVisual artsQuarter (Canadian coin)Media studiesIndependence (probability theory)TasteArtHistorySociologyPsychologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Good Times for Free was an exhibition of a series of photographs of the same title shown at the Fishing Quarter Gallery in Brighton from the 1st-7th of August 2019. \n \nPhotographed in party resorts across the Mediterranean between 2013 and 2016, the series was inspired by my own experience having both visited and worked in these resorts in 2010 and 2011. \n \nFuelled by a thirst for excitement, the need for escape and a taste of independence, those who choose to work in these places enter into a contrasting and often overwhelming world of highs and lows. My aim in returning to photograph these resorts was to highlight this difference; the difference between the neon-coloured, alcohol-distorted, repetitive experience of the night and the stark, bright, sobering experience of the morning after. \n \nBy showing the work on Brighton's seafront my aim was to explore curatorial methods in an unusual and challenging space and to gauge how an audience responds to a body of work associated with a place similar to that in which it is shown. By showing this particular body of work I hope to engage visitors in discussion on a number of topics including youth culture, British cultural influence and the limitations of visual communication.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.592
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.003
Scholarly communication0.0140.009
Open science0.0020.013
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.5920.377

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 head0.067
GPT teacher head0.248
Teacher spread0.181 · 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
Published2018
Admission routes1
Has abstractyes

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