Coding photographic meaning: how interactive digitized surrogates affect photography exhibitions
Bibliographic record
Abstract
This research paper discusses the growing trend of interactive displays in art institutions as a relevant shift in the discourse on photographic literacy, and it is addressed towards curators, archivists, museum professionals, and new media artists with a specialization in photographic studies. The paper explores the concerns of digitized materiality in interactive exhibitions, virtual museums, and image databases. Four cases studies will be utilized in this discussion, including the Peel Art Gallery Museum and Archives, Google Cultural Institute, Flickr: The Commons, and The Lowcounty Digital History Initiative. The relationship between digitized materials and visitors requires an ongoing review of how a diverse demographic of museumgoers read photographs and relate to them in exhibitions. This research paper utilizes topics from new media studies to better understand the implications, benefits, and drawbacks of these different types of displays, and how they fit into the narrative of photographic theory and exhibition design.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".