Rebalancing power relationships in research using visual mapping: examples from a project within an Indigenist research paradigm
Bibliographic record
Abstract
Engaging in respectful relationships is an essential aspect of all research and educational practices. Colonial residue, and the maltreatment and misinterpretation of Indigenous peoples by researchers, puts a great responsibility on the researcher to strive for balance in power relationships within Indigenous contexts. Even more so, in research and education involving Indigenous children diagnosed with autism spectrum disorder (ASD). This may be easier said than done. In a PhD project on the meaning of music for First Nations children diagnosed with ASD in British Columbia, Canada, visual mapping was used to rebalance the power relationships between myself as a researcher and the research partners as a step toward decolonization. The visual maps were used to summarize conversation transcripts that could be used to validate my interpretations and disseminate the research results, create a mutual focal point for negotiating consent and participation and show progress over time. Visual methods, such as visual mapping, are beneficial to individuals with autism, and can also be useful when rebalancing power relations with other research partners, such as parents. In conclusion, visual mapping can be a useful tool for rebalancing power relationships in research and educational practices.
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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 | 0.065 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.048 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".