Traditional First Nations education and socio-cultural theory : Vygotsky's contribution : singing a song to honour my mother
Bibliographic record
Abstract
This is a complex study looking at a number of aspects of historic, cultural and education life of the Tahltan people.Its sources range from personal narrative, records, education documents and learning theory.It seeks to show the possible vital importance of interpreting and shaping a mutually beneficial educational relationship between the Tahltan and the Canadian State.In presenting two worldviews, concepts; First Nations "ways of making meaning" and the pedagogy and implementation of the socio-cultural historic theory of Russian Education Psychologist Lev Vygotsky, I demonstrate substantive commonalities exist between the two.When northern First Nations Elders say teach those kids who they are and where they come from ..., they were saying that a student's knowledge of themselves should be a foundation for their own learning and development.Vygotsky substantiates and articulates that socio-cultural knowledge and self understanding are the foundation to all scientific concept development. Meduh! I "raise my hands" to you all,Thank you, as we say in Tahltan, to those who have supported and encouraged me.Your support, helped "share" the load, and I will never forget that.A special thanks to my family.Meduh for ALWAYS being there.Especially my beloved husband, Julio Amaya-Espana.Meduh!Julio, for your love, kindness, gentle editing, and for reading
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".