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Record W2522266193 · doi:10.1108/oir-02-2016-0044

Networked spectators

2016· article· en· W2522266193 on OpenAlexaff
Jenna Jacobson

Bibliographic record

VenueOnline Information Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCeremonyNarrativeSocial mediaModerationConversationOriginalitySociologyValue (mathematics)Media studiesOpening ceremonyPublic relationsPsychologyComputer sciencePolitical scienceQualitative researchSocial psychologyWorld Wide WebSocial scienceHistoryArtLawCommunication

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to analyse the 2012 Olympic opening ceremony with the goal of making a nuanced contribution to the discussion of online participation and engagement afforded by social media. Design/methodology/approach This paper applies a qualitative approach of sequential video analysis to the 2012 Olympic opening ceremony interpretive segment. Findings Despite the Olympics being a “networked media sport” where countries compete against each another in various sporting events, the paper argues that the overarching narrative of the London 2012 opening ceremony is one that breaks down traditional barriers, while simultaneously situating the individual at the centre of “networked spectatorship”. Originality/value Beyond merely watching media events, the paper proposes the term, “networked spectators” to identify how people participate in the content creation, social media moderation, and conversation using social media. Networked spectatorship moves away from the binary of active and passive participation, and rather reflects on the multiple ways people can engage in media events, which specifically includes social media monitoring/moderation as a form of participation.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0050.006
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0520.006

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 head0.017
GPT teacher head0.318
Teacher spread0.301 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2016
Admission routes1
Has abstractyes

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