Discursive <scp>AI</scp> Infrastructures: Envisioned and Overlooked Museum Futures
Bibliographic record
Abstract
ABSTRACT Prompted by recent innovations, artificial intelligence (AI) is increasingly being discussed across the museum sector regarding its implications for institutional roles and practices. However, AI in particular, is an ambiguous term, a ‘black box’ which is capable of containing and reflecting numerous values and ideals (Crawford, 2021). This paper positions discourse around AI as a ‘discursive’ infrastructure, capable of not only embodying ideals but also shaping and justifying certain institutional practices and roles. This paper thematically analyses 115 pieces of grey literature produced and shared by professional governance bodies in the museum sector from 1995–2023, mainly across Canada, the United Kingdom, and the United States. In doing so, it identifies four preliminary themes encompassing shifts in discourse over time which give shape to a contemporary discursive infrastructure. This prompts timely critical reflections of museum professionals and stakeholders on both imagined and overlooked public roles, responsibilities, and practices of the museum in relation to AI.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.068 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".