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Record W4413417345 · doi:10.63428/1b8hrs55

Anishnabe N’oon Da Gaaziiwin: An Indigenous Peacemaking- Mediation Nexus

2025· article· en· W4413417345 on OpenAlexaboutno aff
John Beaucage, Alicia Kuin, Paul Iacono

Bibliographic record

VenueFourth World Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPeacemakingNexus (standard)MediationIndigenousPolitical scienceSociologyLawEngineeringEcology

Abstract

fetched live from OpenAlex

This article introduces a new dispute resolution process that we have termed a Hybrid Process, which has been designed to support nation-to-nation building in Canada. It is clear that classic theories and current mediation practices are not suited to conflicts that involve Indigenous peoples. In the past, we have used conventional mediation processes with First Nation People and it has not worked because mediation is not an Indigenous cultural practice. When conventional mediation has been employed, First Nation People have often not been participatory and have understandably withdrawn from the process. However, the mediation process is both malleable and adaptable, and the Hybrid Process is built on those solid foundations. A Hybrid Process refers to a combination of two culturally unique practices – Indigenous peacemaking and mediation. The combination of these two practices provides a culturally sensitive and holistic approach to conflict and nation building. This process has been designed for multi-party conflicts involving Indigenous leaders, communities, governments and stakeholders. Due to the complex nature of Indigenous relations in Canada, this process utilizes a team of culturally fluent practitioners to facilitate the process. Designed by a First Nations leader and former Grand Council Chief and two Canadian mediators, this process introduces a new perspective to resolving disputes in a changing landscape and incorporates what we have learned over many years and thousands of mediations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.358
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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