When Further Inclusion of Indigenous People’s Youth Matters: Towards Smart Climate Change Resilient Indigenous Territories, Communities and Diasporas
Bibliographic record
Abstract
This research shows a path to an inclusive entry into the 21st century of a smart changing world on matters related to Indigenous peoples' rights and climate change resilience building. The participation of Indigenous youth in the creation of Indigenous smart climate-resilient territories, landscapes, ecosystems, and communities is presented as a key asset under the Indigenous peoples Rights Global Agenda and the Sustainable Development Global Agenda (SDGA). Political goodwill is needed to enable Indigenous peoples bring up to authorities, to decisionmakers, and other stakeholders, strategic information on their land and community. This information can be observations, life conditions, uncommon situations, threat, damage, or any other event which may occur in their territories, within their communities and on individuals among the people. Being first-hand eyewitnesses of climate change impacts on their lands and in their communities, Indigenous peoples ought to be fully included in decision-making processes on the future of their territories and communities. The methodology adopted to conduct this policy research project consists of two mutually supportive frameworks. The first is a normative framework of relevant human rights is related to Sustainable Development Global Agenda provisions with a focus on the United Nations Declaration on the Rights of Indigenous peoples (UNDRIP). The second is a New Information and Communication Technologies (NICs) plural interface developed under the implementation of the United Nations Biodiversity Global Agenda (UNCBD). This technology framework includes the internet, landscape Geomatics technology with geo-referenced and GIS technology environments, and remote sensing. It also includes mobile phone applications and is enhanced with institutional arrangements consisting of clearing-house mechanisms (CHM) to facilitate and democratize participation of Indigenous youth in the implementation of the normative framework, locally (territory) and within a country with examples in Canada, but also within a region and even globally. This contribution shows that the technology framework will enable the participation of Indigenous youths of several territories and communities within a region and even globally through its online, CHM, and smartphone application components. Keep in mind, however, that political goodwill and the adoption of suitable institutions to protect the privacy of Indigenous peoples and their youth throughout this participation are key prerequisites to any success of including these vulnerable populations in the current smart world. Keywords: UNDRIP, Indigenous youth, e-Public participation to decision-making, Indigenous People’s Human Rights assertion, Smart Indigenous Landscape Geomatics Technology
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".