Playing the Self and Other Otherwise: A B Movie Journey through Low-Expectation Co-Creativity and Outsider Knowledge
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".