Protocol for a Composite Ethnography
Bibliographic record
Abstract
Composite ethnography is a methodological proposition developed by the Concordia Ethnography Lab (CEL) as a way to collectively engage with objects that we know to be multiple. By “taking seriously” the proposition from Science and Technology Studies that the world is enacted through our encounters, our collective research endeavour begins from the principle that collective forms of research produce a world of collectives. For the past two years, as part of EMERGE Matrix’s commitment to building the infrastructures for experimental and collaborative ethnography, a heterogeneous group of researchers at the CEL has been trying to turn such a philosophical commitment to research into an actionable ethnographic protocol. The resulting paper, as well as its accompanying zine and video, depicts The Pit, a project whose endeavour was to use composite ethnography to collectively explore a post-extractivist site in Montreal as a space for future-making. In this paper, we describe the steps towards the composite, showcasing the practical, pedagogical, ethical, and epistemological issues that arise when trying to render the tacit explicit in collaborative ethnographic research, including the tensions between normative ideals on what ethnography is and the challenges of making messy research processes travel.
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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 | 0.077 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.259 | 0.065 |
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".