Indigenous people and economic development. An international perspective
Bibliographic record
Abstract
Indigenous peoples are an intrinsic part of countries like Australia, New Zealand, Canada, Finland, USA, India, Russia and almost all parts of South America and Africa. A considerable amount of research has been done during the twentieth century mainly by anthropologists, sociologists and linguists in order to describe, and document their traditional life style for the protection and safeguarding of their established knowledge, skills, languages and beliefs. These communities are engaging and adapting rapidly to the changing circumstances partly caused by post modernisation and the process of globalization. These have led them to aspire to better living standards, as well as preserving their uniqueness, approaches to environment, close proximity to social structures and communities. For at least the last two decades, patterns of increased economic activity by indigenous peoples in many countries have been viewed to be significantly on the rise. \nIndigenous People and Economic Development reveals some of the characteristics of this economic activity, 'coloured' by the unique regard and philosophy of life that indigenous people around the world have. The successes, difficulties and obstacles to economic development, their solutions and innovative practices in business - all of these elements, based on research findings, are discussed in this book and offer an inside view of the dynamics of the indigenous societies which are evolving in a globalised and highly interconnected contemporary world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".