Bringing researchers and resources together : the Atiku northern and arctic studies portal
Bibliographic record
Abstract
The goal of this presentation is to introduce a new bilingual information portal for Northern Studies in Quebec, Canada, and describe the challenges and opportunities that arose from the creation of this multidisciplinary, multi-institution web portal. This project has brought together not only the library resources of the three institutions supporting Northern Studies but also the librarians who support these diverse and interdisciplinary researchers across Quebec. Collaboration between the members of the Institut nordique du Quebec (INQ)--a research centre bringing together more than 150 researchers from three universities (Institut national de recherche scientifique, Université Laval, McGill University), and representatives \nfrom Indigenous groups and the public and private sectors--led to the creation of this project. The portal was created to facilitate collaboration between INQ members and for anyonen interested in Northern Studies by bringing together multidisciplinary content from a variety of sources and modes of access (paid and open). It reflects a transdisciplinary approach that is increasingly required in Northern Studies, an approach that seeks to harness the resources and expertise associated with several different fields of study in order to understand a complex issue. The problem of access for non-university users will be addressed, particularly for Indigenous communities and the Quebec government. The librarians supporting Northern \nStudies at these three universities had not previously worked together, and the project allowed for a new collaboration between libraries and librarians. The flexible way in which the project has been developed allows for new institutions and librarians to join.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".