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Record W7154008338 · doi:10.60633/tfj.i06.125

Playing the Self and Other Otherwise: A B Movie Journey through Low-Expectation Co-Creativity and Outsider Knowledge

2025· article· en· W7154008338 on OpenAlexaff
Jared Epp

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEthnographySelfAbsurdismHappeningElement (criminal law)Space (punctuation)Nothing

Abstract

fetched live from OpenAlex

My co-creator David Ross and I made what we called ‘ethnographic B movies’ as the central element of my doctoral fieldwork. These low budget and cheesy speculative fiction films, written primarily by David, became an opportunity for him to share his ideas of a revolution based on the concept of the Musicality of Reality. The films gave David a chance to perform himself otherwise: no longer on the socio-economic margins, as a failed academic living on a senior’s fixed income, but as a legitimate thinker leading a global movement. Together, we gave into and created a mad world of possibility, a place where I could also become an Other to my ethnographic self, performing a version of my actual self as always unsure about what was happening in the field. In this visual essay, I share a series of film stills in a montage-like fashion and with an accompanying text, to mirror the absurd silliness of our films inspired by the B movie genre. I focus on moments across our filmic output that highlight how giving into playfulness provided very serious self-disclosure opportunities both for David and for myself. By bringing the reader into the space of our films in this way, I want to encourage others to experiment and play with the possibilities of ethnographic becoming afforded by low aesthetic expectations and absurdist creativity.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
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
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.276
GPT teacher head0.596
Teacher spread0.319 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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