The Benefits of a Photograph and Image Cataloguing Database for Research and Archival Purposes, Illustrated by an Example from Canadian Archaeology
Bibliographic record
Abstract
The practice of using photography, whether in digital, slide, or print form, is a fundamental method of documenting and preserving finds and information in archaeology and most other museum-related disciplines. Images play an important role in the communication and preservation of information and can be regarded as archival collections in their own right. However, in many situations it is difficult to store and search efficiently through this vital resource. With the advent of desktop databases and interconnectivity, images can be readily organized into a searchable database. This approach becomes especially useful when dealing with the huge numbers of photographs accumulated through large projects. The EPIC database is a good example of the solution to this problem. EPIC was created to deal with images generated through one research centre of a large archaeological project (SCAPE: Study of Cultural Adaptations in the Canadian Prairie Ecozone). Built around off-the-shelf software, EPIC allows users to view a small thumbnail of an image with associated information, and has been designed to facilitate multiple search pathways. It also has the ability to link to related Museum databases. EPIC has proved beneficial not only to the SCAPE research community, but also to others who have used the information generated through the project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".