Examining an Apparent Educating Gap Between Non-Indigenous and Indigenous Learners: A Hermeneutic Phenomenology Approach
Bibliographic record
Abstract
This hermeneutic phenomenology study examined the apparent phenomenon of the educating gap – a persisting disparity between the educational experiences and results of Indigenous and non-Indigenous learners in a Kindergarten to grade 12 (K-12) public education system. I drew on the lived stories of non-Indigenous and Indigenous educational stakeholders as research participants to look for the presence or absence of deficit model language or for alternate explanations in their personal accounts of this disparity. The research setting was the North-Central, East Coast of Vancouver Island, British Columbia, Canada. Under the umbrella of an indigenist perspective, while employing a method of narrative interviews and their analysis through a hermeneutic circle, this apparent educating gap was studied in terms beyond what can be learned through statistics and data, as a multi-dimensional phenomenon. Interviews and the ensuing analysis did not show any evidence of deficit model language used in participant narratives of this apparent educating gap. In place of such deficit explanations, the research revealed perceptions and perception patterns, pointing to hindrances or obstacles that disrupt equal learning opportunities for Indigenous students, families, and communities. These perceptions came to light in the participants’ life stories that outlined their experiences of the strengths and struggles in attempting to reach every learner equally. For the purpose of this research, the terms perception and perception patterns were used to point to participants’ experiences and insights related to the disparity of this apparent educating gap.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".