Adapting to born-digital photographs : a case study of the Canadian Centre for Architecture
Bibliographic record
Abstract
As born-digital photography collections grow, so grows the need for a more defined set of guidelines on the best practices of how to acquire, describe and preserve said photographs. This paper is the result of a practical project conducted at the Canadian Centre for Architecture. The project uses the CCA as a case study and asks the question: how is the Centre adapting to the collection of born-digital photographs? During a six-month residency, the author, developed and implemented a description guideline, workflow, and donor guideline for the collection and cataloguing of born-digital photographs. These guidelines are the result of the steady growth in the volume of born-digital photographs coupled with the need for ensuring long term preservation for existing and potential collections. The aim of this paper is to help improve the usefulness and clarity of the guidelines. The case study and paper was conducted in conjunction with the requirements of Film and Photography Preservation and Collections Management (FPPCM) master’s at Ryerson University. The title of the guideline is Guidelines for Describing Born-Digital Photograph.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".