Understanding Community Needs: A Step Closer to a Digital Library for Communities in Canada’s North
Bibliographic record
Abstract
This paper provides insight into the findings from a survey conducted with community members in the Inuvialuit Settlement Region (ISR) in Canada’s North. The survey was conducted to develop a deeper understanding of needs and information seeking behaviour of users in ISR. The findings from the survey will be useful in developing a digital library (DL) platform for communities in ISR. Cet article donne un aperçu des résultats d'une enquête menée auprès des membres de la communauté de la région désignée des Inuvialuit (RDI) dans le Nord du Canada. L'enquête a été menée afin d’obtenir une meilleure compréhension des besoins et des comportements de recherche d'information des utilisateurs dans la RDI. Les résultats de l'enquête seront utiles au développement d’une plate-forme de bibliothèque numérique (BN) pour les communautés en RDI.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".