Structure from motion: the movement and digital modelling of an artefact from the Blackfoot collections, British Museum
Bibliographic record
Abstract
In the field of computer vision, Structure from Motion (SfM) is a photogrammetric technique for building three-dimensional models from two-dimensional imagesequences. This paper discusses a knife currently held in the Blackfoot collections of the British Museum and its digital modelling using SfM photogrammetry. We also explore the potential of thinking with the concept of structure-from motion as a research methodology. To do this, we take a cue from Sara Ahmed’s work on the potential for queer use as a way of reanimating the project of diversity work and opening up institutions to those who have been excluded (Ahmed 2019). Can we then repurpose the idea of SfM to think about the mobility of objects and people through time and space? What structures are compiled and made visible by tracing the movement of an object from Southwest Alberta, Canada, to a UK museum store? What kind of futures are implied? This paper is also premised on the work of the Concepts Have Teeth project, which borrows its title from Mohawk anthropologist Audra Simpson (2007). Simpson describes the differential power of one account over another in establishing the terms of being seen or being present: Western philosophical histories of seeing, knowing, and visualising, tied to legal fiat, enable disproportio nately empowered political forms that compound the lack of visibility of and access to subaltern histories. Focusing on issues around access, tangibility, materiality, and self-representation, the project explores the potential for a new conjunction of art practices with digital technology to open access to collections and develop new contexts and associations by rereading or counter-mapping existing archive material.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".