Implementing Indigenous Education with Technology Education in British Columbia
Bibliographic record
Abstract
This project begins by outlining why Indigenous education and technology education need to be more closely connected. It begins by exploring a framework which explains the connections between the First People’s Principles of Learning and social constructivism theory. Indigenous education is explored touching on important topics, such as residential school history, reconciliation, racism, and decolonization. This topic leads into exploring Indigenous education, discussing why it is important and the challenges faced by educators when implementing it. The following section explores technology education by defining technology and technological interaction. These topics all come together to explore the intersections between Indigenous education with technology education. It then looks at how technology education can be viewed through a holistic lens by incorporating the self, family, community, land, spirits, and ancestors. Further, it explores generational roles and responsibilities and sacred knowledge and its connections to the classroom/shop. The projects main focus is the creation of a website where technology education teachers in BC (and elsewhere) can go to find resources and classroom approaches with an Indigenous education focus which can be readily implemented for a technology education shop/classroom. Further, the website offers a template to follow so that educators can also submit their own lessons or projects to be shared with others. The focus is to help technology teachers address the large void often present in the technology education curriculum regarding Indigenous ways of knowing, being, and doing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.017 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".