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Record W4210971898 · doi:10.26522/jess.v4i.3714

Houston College Sport Programs’ Hurricane Harvey Communication

2022· article· en· W4210971898 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Emerging Sport Studies · 2022
Typearticle
Languageen
FieldComputer Science
TopicTechnology and Security Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperTheme (computing)Content analysisSocial mediaSociologyMedia studiesLibrary scienceAdvertisingHistorySocial scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This study provides a Twitter content analysis of tweets by Houston-based Division I college sport programs during Hurricane Harvey. A content analysis was performed on the tweets appearing on the main intercollegiate athletics Twitter pages of University of Houston, Houston Baptist University, Prairie View A&M University, Rice University, and Texas Southern University in response to Hurricane Harvey. The researchers based their study on grounded theory informed by a study conducted by Inoue and Havard (2015). While this study examined tweets rather than newspaper and magazine articles like Inoue and Havard (2015), this study confirmed the theme findings in Inoue and Havard (2015) applied well in a Twitter social media setting as well. New themes that were added by the researchers in the current study proved to be applicable.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.294
Teacher spread0.262 · 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