Decolonizing Evaluation of Indigenous Guidance and Counseling Approaches: A Review of Selected Evaluated Programs
Bibliographic record
Abstract
The concept of Indigenization of research has been increasingly explored in recent studies, with emphasis placed on the ontological, epistemological, and axiological perspectives of Indigenous peoples to find effective solutions to their challenges. This also applies to the evaluation of guidance and counseling approaches in Africa and other nations, where Indigenous therapies are developed based on different philosophical foundations, such as Ubuntu (Africa). Relational ontologies and epistemologies appear to be common across various Indigenous nations in Africa, Australia, Canada, and North America. This article analyzes studies from these regions on evaluations of Indigenous guidance and counseling therapies. The majority of the evaluations use conventional paradigmatic assumptions in their approach, rather than relational models that are participatory and respectful of participants’ worldviews, including the living, non-living, metaphysical, and spiritual aspects of Indigenous people. However, the Indigenous therapeutic programs analyzed in this study incorporate culturally appropriate activities and curricula that align with relational axioms. This article proposes the use of relational models of evaluation to assess Indigenous counseling programs, where researchers can draw conclusions that align with the cultural contexts of the Indigenous people being researched.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".