Understanding Digital Literacy Training Success: An Exploration Across Canada
Bibliographic record
Abstract
This paper reports progress of a SSHRC-funded research investigation that studies the factors affecting the success of digital literacy skills training offered by community-led organizations, such as public libraries, across Canada. The goal of the study is to identify best practices. The study also seeks to contribute to the theoretical understanding of digital literacy instruction led by community organizations. This paper reports preliminary results of the analysis of interviews with administrators and instructors from organizations in Canada which offer such training, as well as from interviews and surveys collected from people who took part in these organizations’ training activities. Cet article fait état de l'avancement d'une recherche financée par le CRSH qui étudie les facteurs influant sur le succès de la formation en littératie numérique offerte par des organismes communautaires, comme les bibliothèques publiques, partout au Canada. Le but de l'étude est d'identifier les meilleures pratiques. L'étude cherche également à contribuer à la compréhension théorique de l'enseignement de la littératie numérique menée par des organisations communautaires. Cet article présente les résultats préliminaires de l’analyse des entrevues avec des administrateurs et des formateurs d’organismes au Canada qui offrent une telle formation, ainsi que des entrevues et des sondages recueillis auprès de personnes ayant participé aux activités de formation de ces organismes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".