Identity Information in Object-Level Descriptions: Towards Inclusive Cataloguing Contemporary Photography
Bibliographic record
Abstract
This research follows an epistemological questioning among archival practitioners who are challenging methods of historical recordkeeping. The National Gallery of Canada’s contemporary photography holdings serve as a case study to question what information is deemed necessary in records documentation and the ways that information reaches the archive. I propose including self-disclosed object-maker identity information in museum databases to better contextualize, and enable the interpretation of collection objects and allow more accurate representation of artists and makers. Self-disclosed identity information shifts knowledge production away from the institution to the object-maker, decentering information and power structures away from collecting institutions. This study presents the Inclusive Cataloguing Toolkit as a practical solution. Its creation and implementation is inspired by contemporary archival documentation practices such as participatory description, archival autonomy, and record co-creation that relocate information, knowledge, and power structures to bridge the needs of inclusive cataloguing and provide a framework for future change.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 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".