e-artexte: Open Access Digital Repository for Documents in Visual Arts in Canada
Bibliographic record
Abstract
Founded in 1980, Artexte is a not-for-profit organization with a mandate to support the advancement of the visual arts through reliable information sources. Artexte engages in research, interpretation and dissemination activities based on its unique collection of bibliographic materials, covering the visual arts from 1965 to the present, with emphasis on the art of Quebec and Canada. \n \nThis year, Artexte launched e-artexte (http://e-artexte.ca), a unique open access digital repository for documents in the visual arts. The e-artexte self-archiving repository caters to the needs of museums, galleries, artist-run centers and other publishers/authors in the visual arts community who are looking to make their publications more widely accessible via the Internet. Open access is a founding principle of the e-artexte service. \n \nThe e-artexte repository is the result of research, development and design that took place over the last three years. This demonstration will present the results of a metadata migration and software customizations for providing access to Artexte bibliographic data using the EPrints (http://www.eprints.org) open source digital repository software. The e-artexte interface and policies will be demonstrated. The demonstration will provide an overview of the e-artexte service and its self-archiving functionality. \n \nThe open source EPrints platform that powers e-artexte is highly interoperable, allowing for the export and harvesting of its collection by researchers and aggregating software. In choosing open source technology that is capable of export of e-artexte contents using semantic web standards, a necessary condition for innovation is met. e-artexte enables researchers to leverage the open metadata exporting capabilities of the EPrints software to create specialized interfaces. An example of such visualization will also be demonstrated: \n \nTimeline Visualization: Photography Exhibition Catalogues in Artexte Collection (1960-2012). PhotographyMedia.com http://www.photographymedia.com/visualizations/artexte/e-artexte-1.html \n \nThis example demonstrates how e-artexte metadata can be visualized using the SIMILE (Semantic Interoperability of Metadata and Information in unLike Environments) widgets originally developed by MIT (Massachusetts Institute of Technologies) Libraries and CSAIL (Computer Science and Artificial Intelligence Laboratory).
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.128 | 0.033 |
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".