Bibliographic record
Abstract
Given the present pace of educational globalization, educators – especially in rural schools – will benefit from an awareness of traditional knowledge as a significant contributor to sustainability. Many countries operate through a system whereby major decision making, especially in such areas as education and health, emanate from state levels of governance; and these decisions are often uni-directional. In education this implies a ‘one size fits all’ philosophy that forces educator compliance with accountability driven pedagogy and curricula subjected to competitive standardized testing processes that are caught up in market driven values. Aims of a research pilot (Harris & Barter, 2011-2012) in one province in Canada, and through a critical pedagogy of place and participatory research, were to introduce students and community members in rural areas to issues of local relevance; to develop school curricula that call for students-as-researchers; and, as an example of local knowledge and relevance, to focus on food practices (past, present, and as future possibility). Initial findings of the pilot indicate the changing nature of rural life that includes a move from remoteness to semi-isolation brought about with the building of roads and effective ferry connections, transient populations seeking seasonal employment, and a growing awareness of people’s need to revive past practices of food production. We found as well, examples of extraordinary innovation in food cultivation and food harvesting that drew on local and traditional knowledge. Finally, we experienced success in incorporating traditional knowledge into existing curriculum. This we did by varying ways of knowing and experiencing through the arts. For us as researchers, two important concepts permeated this research, critical pedagogy of place and traditional knowledge. In this paper, situated in a framework of cultural and social resistance to economic globalization, I describe initial efforts of the pilot project in expanding the discourse of rural studies, exploring traditional and local rural knowledge, and increasing rural agency. While providing a brief overview of the project, its specific purpose is to highlight the relevance of traditional knowledge as a significant contributor to sustainable global growth. I think of the potential that such knowledge, which often originates from rural environments, has to represent an alternative to consumer dependence.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".