Bibliographic record
Abstract
Purpose The purpose of this paper is to present the main features of Archimède, which is the institutional repository system developed by Université Laval to address its specific needs. Design/methodology/approach These needs include the availability of a multilingual interface, the possibility to simultaneously index metadata and full text, and the compatibility with multiple technological infrastructures. The privileged approach relied on open source softwares and the use of automatic code generation tools in order to lower development costs and time. This led Université Laval's team to the creation of an institutional repository system that is based on Java technology and which is not OS‐specific. Findings The system offers: documents management functionalities; dissemination mechanisms compatible with OAI‐PMH2 (Open Archive Initiative Protocol for Metadata Harvesting V.2.0); an indexing and searching framework (LIUS) that can index over ten documents formats; and a selective dissemination of information service. Archimède and LIUS are now distributed under a GPL licence. Further developments will extend the metadata formats range supported by Archimède and will include archive management functionalities. Originality/value This experience shows that the development of an institutional repository system resting on open source softwares, frameworks and application program interfaces could lead to impressive results, in a short amount of time and with a minimum of investment.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".