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Record W4412514073 · doi:10.1386/punk_00296_7

I heard they suck at interviews (but we edited it for clarity): A conversation with Fat Mike of NOFX

2025· article· en· W4412514073 on OpenAlexaboutno aff
Ellen Bernhard, Stefano Morello, Paul Fields

Bibliographic record

VenuePunk & Post Punk · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsConversationCLARITYPsychologyMedia studiesSociologyCommunicationChemistry

Abstract

fetched live from OpenAlex

‘Fat’ Mike Burkett is the former vocalist, bass player and songwriter of NOFX. At the 2025 Punk Scholars Network US/Canada Conference , hosted at the Punk Rock Museum in Las Vegas, Fat Mike took part in a roundtable conversation with Ellen Bernhard, Stefano Morello and Paul Fields. The conversation focused predominantly on an upcoming publication of scholarly essays on NOFX, which Bernhard and Morello co-edited with David Pearson, and which Fields contributed to. Fat Mike discusses his views on the publication, appraises and adds further context to some of its chapters and reflects on punk scholarship in this informal interview. The interview was not originally intended for publication, therefore it has been edited for clarity.

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)0.010
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0200.018
Scholarly communication0.0080.011
Open science0.0010.005
Research integrity0.0060.017
Insufficient payload (model declined to judge)0.0090.002

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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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