A Community and Culturally Centred Approach to Secondary Vocational Education and Training in a Remote Northern Indigenous Environment.
Bibliographic record
Abstract
his thesis is a study of how secondary vocational education and training(SVET) can be developed to meet the cultural, labour and community-based needs of the local and regional econ-omies in a Northern remote Indigenous context. The region of Eeyou/Eenou Istchee in Northern Quebec provides the regional setting and helps define the challenges and potential of SVET through community engagement. There are three themes that will be explored in order to develop the foundations of SVET, Traditional Academic Pathways (TAP), school to work and essential skill development, and purposeful community centred learning.Traditional Academic Pathways (TAP) defined through the colonial history of Canada are viewed with distrust and have dismantled and excluded the Indigenous Learning Paradigm. The review of the literature along with interviews with the studies participants, understanding TAP and integrating lessons learned are integral to adopting transformative Indigenized SVET. The discourse pertaining to school to work programs and vocational education and training in Canada at the secondary level is contentious as it has historically held a lower status then the traditional academic system. This is further exacerbated when Secondary Vocational and Training is considered in a Northern Indigenous context in Canada. School to work programs and vocational pathways are limited by Western-centric perspectives of that has traditionally focused concepts such a Human Capital Theory and does not engage with Indigenous cultural values. SVET in a Northern Indigenous context must be developed to not only to allow learners to participate in the labour market and the regional economy but also supports community capacity building through Purposeful Community Centred Learning.Purposeful Community Centred Learning(PCCL) is an opportunity to weave Western and Indigenous learning paradigms to developing core learning programs that benefit the community. Community engagement and understanding local knowledge systems from an Indigenous cultural perspective can help develop SVET into an Indigenized holistic approach to learning
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".