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Record W2765970391

Spoiler und Spoilerkonsum bei Filmfranchisefans

2017· article· de· W2765970391 on OpenAlexfundno aff
Matthias Völcker

Bibliographic record

VenueSocial Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · 2017
Typearticle
Languagede
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsnot available
FundersYork UniversityTemple University
KeywordsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Im Mittelpunkt des Aufsatzes steht eine bisher kaum erforschte kommunikative Praxis bei (Filmfranchise-)Fans, die v.a. in den Sozialen Netzwerken beobachtet werden kann und dort z.T. äußerst kontrovers diskutiert wird. Im Mittelpunkt stehen sogenannte "Spoiler" wie auch ihr Konsum. Der Aufsatz greift die Ergebnisse einer empirischen Untersuchung mit Star Wars-Fans auf und stellt die Resultate einer Interviewstudie und einer Fragebogenuntersuchung über Spoiler vor. Das empirische Material zeigt nicht nur Charakteristika und die (emotionale) Relevanz einer populärkulturellen Fan-Praxis auf, sondern stellt zugleich die interaktive und identitätsbezogene Bedeutung dieses Phänomens heraus.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.423
Teacher spread0.342 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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