Mapping the Evolution of Technology Use Among Indigenous Minorities: A Bibliometric Analysis (2000–2024)
Bibliographic record
Abstract
ICT use by indigenous minorities has gained much attention in research due to enhanced technology that has culminated in the closing of the digital divide, social justice, and cultural diversity. The current bibliometric analysis is conducted with the use of VOS viewer version 1.6.20 with key findings indicated in the research between 2000 to 2024. It implemented that the number of publication has been constantly rising, significantly between 2015 to 2021. Their productivity is also notable, as among the 60 contributors three of them have three articles and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 2}$</tex> citations: Katherine M. Conigrave, James H. Conigrave, Scott Wilson, and Jimmy Perry. Such counties include the US, Australia, and Canada; the US has the most citations. Interestingly, such terms as ‘Internet,’ likewise, Indigenous population,’ and ’human’ are most used in the contexts proving important investigation regions. In this paper, the author aims to give a primary understanding of new technology adoption among indigenous tribes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.280 | 0.483 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".