Implementing the General Education Development (GED) Program in First Nations Communities: Struggles for Power
Bibliographic record
Abstract
This paper describes an ethnographic case study of eleven First Nations adult learners in a Northern Ontario community attempting to earn secondary school equivalency through the General Education Development (GED) program. The paper maintains a focus on the power differentials at work in both the learners' prior educational endeavours and their experiences while working inside of the GED program. Based on the data, we argue that, despite appearances to the contrary, First Nations learners are subjected to a system of education that is failing to empower those learners to take advantage of educational opportunities that are available in mainstream Canadian society. Cet article décrit une étude ethnographique de cas auprès de onze apprenants adultes autochtones qui tentent, dans une communauté du Nord de l’Ontario, d’obtenir une équivalence d’études secondaires par des cours de formation générale (GED). Plus précisément, l’article porte sur les écarts systémiques de pouvoir qui entrent en ligne de compte tant dans les études préalables des apprenants que dans leurs expériences avec le programme GED. En s’appuyant sur les données, nous affirmons que, même si les apparences peuvent laisser supposer le contraire, les apprenants autochtones sont soumis à un système d’éducation qui ne réussit pas à les doter des compétences nécessaires pour saisir les possibilités éducatives offertes par la société canadienne.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".