Approaching Canadian adult literacy research as a community of practice:Implications and possibilities
Bibliographic record
Abstract
In recent years there has been significant interest in increasing educational research capacity in many countries and many fields, including Canadian adult literacy and numeracy education. This article asks what we can learn by taking a community of practice approach to research capacity, using as an illustration the work of the National Literacy Secretariat 1998-2003. Analysis brings forward a number of key recommendations useful to consider in discussions of educational research capacity. Implications are that communicative infrastructure is vital, social networks are key, current research needs to be better integrated with past research, and that increasing research capacity goes hand in hand with strengthening community. Résumé Depuis quelques années, un intérêt substantiel s’est développé dans différentes branches de recherche et dans de nombreux pays portant sur l’accroissement de la capacité pour la recherche éducationnelle. Cette tendance s’est également produite dans les domaines de l’alphabétisation et de la numératie des adultes au Canada. Cet article présente les leçons à tirer d’une approche basée sur les communautés de pratique, en prenant comme exemple le travail du Secrétariat national à l'alphabétisation. L’analyse présentée permet d’identifier des recommandations clés à considérer dans les discussions sur le développement de la capacité de recherche. Cette étude démontre que l’infrastructure de communication entre les membres de la communauté est primordiale, que les réseaux sociaux sont d’une importance clé, que les recherches actuelles auraient besoin d’être mieux intégrées aux précédentes, et que l’augmentation de la capacité de recherche va de pair avec le renforcement de la communauté elle-même.
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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 | 0.116 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.048 | 0.045 |
| Scholarly communication | 0.035 | 0.016 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".