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Record W2903255135 · doi:10.1177/1532708618809723

“I Wish I Could Grow a Full Beard”: The Amateur Pogonotropher on the Beardbrand YouTube Channel

2018· article· en· W2903255135 on OpenAlexaff
Christopher J. Schneider

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2018
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsBrandon University
Fundersnot available
KeywordsAmateurSocial mediaWishExploratory researchSociologyMedia studiesScholarshipChannel (broadcasting)Social scienceWorld Wide WebPolitical scienceComputer scienceLawAnthropology

Abstract

fetched live from OpenAlex

Pogonotrophy refers to beard cultivation including growth and grooming practices. This exploratory study contributes to the little understood role of beard culture on YouTube. Scholarship examining the relationship between social media platforms such as YouTube and beard culture is almost nonexistent. This gap in the research allows us to ask the following: What sorts of content do users circulate about beards on YouTube? And, how does this content contribute to how users interact and learn about beards? A total of 62,061 user-generated comments across 310 videos featured on the Beardbrand YouTube channel were collected and examined using qualitative media analysis. Three themes emerged from an analysis of these data: the yeard quest, the ideal type, and how to beard. The findings illustrate the important role that YouTube plays in fostering contemporary beard culture. Suggestions for future research are noted.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.008
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.163
GPT teacher head0.407
Teacher spread0.244 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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