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Record W4402190954 · doi:10.3998/jep.6098

No One Is In Trouble: Queer Feminist Collaborations in the Amplify Podcast Network, <em>The SpokenWeb Podcast</em>, and Witch, Please Productions

2024· article· en· W4402190954 on OpenAlexaff

Bibliographic record

VenueJournal of Electronic Publishing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntellectual Property Law
Canadian institutionsWilfrid Laurier UniversityUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

At Witch, Please Productions—the queer feminist media production company I co-founded with Marcelle Kosman and Hannah Rehak—we have a motto that underpins and guides our collaborative ethos: “no one is in trouble.” Our insistence that no one is in trouble is rooted in our queer feminist ethics of care, one that prioritizes the well-being of our collaborative team and by extension our larger community of collaborators, interlocutors, and listeners. While stated overtly and frequently at Witch, Please Productions, this care-based ethos of collaborative media creation emerged gradually for me through various collaborative projects, including The SpokenWeb Podcast and the Amplify Podcast Network, both projects that were also, notably, built through queer feminist collaborations. By prioritizing care and well-being from the beginning, and building projects from the ground up with that ethos in place, we are collectively learning new ways to make things together. This article takes the form of a conversation with some of my key collaborators, modeling the playful collectivity of these projects, to match in form what I am articulating in content: that we create more radical, expansive, collaborative scholarship when we center care, relationships, and the well-being of the collective.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0060.004
Open science0.0010.000
Research integrity0.0000.003
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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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