Exploring Indigenous education leadership research in Canada, the United States, Australia and New Zealand
Bibliographic record
Abstract
Purpose This literature systematically reviews articles published in “core” international journals on the topic of Indigenous education leadership over the period from 2000 to 2018 in four English-speaking countries, covering Canada, America, Australia and New Zealand, in which all of them have long colonial history and Indigenous population. These reviews provide insights into the nature of this emergent literature and generate many implications that required for further research in Indigenous education leadership. Design/methodology/approach In this study, a vote counting method was employed and a clearly delimited body of research on Indigenous education leadership was also identified. The vote counting method can enlarge the perspectives on the noticeable heterogeneity of Indigenous education leadership within the four English-speaking countries. This is the basic constitutive element for the development of a comparative literature in Indigenous education leadership. Moreover, this method can clearly calculate the annual number of articles about Indigenous education leadership, and the various methods used in the publications of Indigenous education leadership can be figured out as well, which helps to find out the different patterns of changes on Indigenous education leadership. Findings This study identifies the patterns of Indigenous educational leadership research across four English-speaking countries, which will contribute to the development of research in this regard. Originality/value This is one of the first studies about Indigenous educational leadership in the world. It will not only contribute to education practice but also leadership theory development.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".