An innovative response to enhance Native American success and advancement in higher education
Bibliographic record
Abstract
This thesis argues the need for major change in higher education options currently available to Native American students in the United States. Universities and Tribal Colleges represent the most common choices that Native students opt for in seeking degrees in tertiary education. However, for the most part, Universities and Tribal Colleges are not working effectively enough to produce the levels of success that are significantly transforming of the wider social, economic and cultural crisis conditions within many Native American communities. This thesis will focus on how to develop a major transformation of the higher education sector generally, a focus which also positively includes the underdeveloped potential that lies within the Tribal Colleges and Native programs in various university sites. \n\nThis thesis attempts to clarify what has gone wrong in the higher education of Native Americans and to propose a national, innovative strategy for intervention. Identifying what is problematic in existing approaches will build critical insights that will inform the new strategies for change. The overall argument is that new institutions which are more sensitive and responsive to Indigenous aspirations first and foremost, need to be considered as a key in transforming Native American higher education performance.\n\nRather than define absolutely all of the possible ingredients of what might be included in a new higher education model, this thesis works first to identify and aggregate a number of key barriers and constraints by collating different information streams. Once identified these critical elements, practices, values and structures that are deemed to be the major barriers to Native success are then used to inform the proposed new institutional framework. While a single institution model is ultimately proposed by this thesis, it should be regarded as an answer, not the answer. A broader intention of this thesis is to bring more focus to this area of concern and underdevelopment within Higher Education and suggest that there are different answers and possibilities (as the Maori examples have demonstrated) that are truly innovative, and which can profoundly impact Native American individual and community social, economic, cultural and political development and advancement.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".