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Record W4283389482 · doi:10.26522/jess.v7i.3969

American Football and Facilities at the University of Colorado

2022· article· en· W4283389482 on OpenAlexvenueno aff
Chad Seifried, Brent D. Oja, Alan R. Morse

Bibliographic record

VenueJournal of Emerging Sport Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
FundersAnschutz Medical Campus, University of Colorado
KeywordsFootballInstitutionEliteHigher educationPolitical scienceCompetitor analysisCollege footballPublic relationsPublic administrationSociologyMarketingBusinessLawPolitics

Abstract

fetched live from OpenAlex

The present study sought to understand more about which individuals or groups influenced the development of the University of Colorado’s (CU) football grounds (i.e., American football) and to determine how their evolving complexity (e.g., increasing size, services, technology, and capacities) shaped the image of an elite institution and the cultural ascension of the Rocky Mountain region in the Western United States. More specifically, the current research showcases how football and early playing grounds served as institutional social anchors and influenced or addressed enrollment growth, cultivated alumni relationships, produced or rallied financial support, and promoted the school. Next, the present study determines whether the various construction projects and renovations of CU football athletic grounds match the larger pattern practiced by other institutions of higher education, both regionally and nationally. Finally, the current research demonstrated that CU used football and its stadia, like other institutions of higher education, as a strategic social anchor for students, alumni, local (i.e., Boulder) community members, and businesses to interact or engage one another. As a social anchor, CU football, and its related facilities, helped the institution create and maintain a unique institutional identity among its competitors.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.304
Teacher spread0.264 · 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 teacher head, not a consensus.

Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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