Challenges and Responsibilities Facing Canadian Literacy Researchers Working in Global Communities
Bibliographic record
Abstract
This article addresses issues facing Canadian literacy researchers who are working in global contexts and particularly the potential complications that arise when research conducted in developing countries is funded by sources such as international aid institutions, foreign governments, non-governmental organizations, and donor-based organizations. We focus especially on the issue of development of local research capacity and expansion of the knowledge economy. We first create a framework by describing the types of literacy projects funded by alternatives to the standard research grants of government agencies. We next review tensions that can arise between researchers and these types of funding organizations. We then turn to examples of current Canadian literacy research carried out in developing countries that provide guidance in designing, conducting, and publishing locally-empowering and globally-connected research.
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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 | 0.101 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.084 | 0.039 |
| Scholarly communication | 0.031 | 0.008 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".