The Pop-Up Museum of Legal Objects project: an experiment in ‘socio-legal design’
Bibliographic record
Abstract
This article explores the strategies underlying the Pop-Up Museum of Legal Objects, a project based on two collaborative events in which design-based practices were deployed to further socio-legal research. Like other endeavours focusing on legal objects, the Pop-Up project produced a collection of object-based commentaries of diverse geographical, historical and material origins – from Australia to Canada to Egypt, 1200 BCE to the present day, bark to gold to plastic. What renders the Pop-Up project distinctive among interventions in the ever-deepening legal object landscape is, first, that it aims not only to generate new knowledge about objects and about law, but also to transform research behaviours; and, second, that it pursues those aims by adopting design-based practices and experimental attitude. The paper sets out the specific roles played by model-making in each event and the experience design underpinning the project as a whole. Participant feedback collected during and after the events is used to widen the perspective throughout. The article concludes with an indication of how such model-making might extend beyond the museum into fieldwork, using an example from the author’s own practice around an ox-hide copper ingot from Cyprus.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".