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Record W2973088341 · doi:10.1139/geomat-2018-0019

Conflict between Indigenous land claims and registered title: case studies from Canada and Kenya

2019· article· en· W2973088341 on OpenAlexafffundvenueabout
Dennis Mbugua Muthama, Erin L. Tompkins, Michael Barry

Bibliographic record

VenueGEOMATICA · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsGeological Survey of CanadaUniversity of Calgary
FundersUniversity of Calgary
KeywordsRestitutionIndigenousSettlement (finance)TribunalLand rightsColonialismPopulationGeographyHuman settlementCompensation (psychology)Land useLand tenureLand grabbingCommunal landPolitical scienceLawSociologyEnvironmental planningBusinessArchaeologyEcologyAgricultureFinance

Abstract

fetched live from OpenAlex

Two case narratives illustrate the difficulties in resolving historical land restitution in different contexts. Cases from Canada and Kenya illustrate how different land conflicts between Indigenous land rights and registered title may be addressed. In Canada, Williams Lake involved an Indigenous community with a long settlement history in the region with a claim going back to early European settlement. In Kenya, Waitiki Farm involved a post-colonial population established by local Indigenous and migrant groups. The Williams Lake decision resulted in a First Nations land claim being settled in the form of monetary compensation in a dedicated tribunal. The Waitiki Farm decision led to a negotiated settlement in which the owner was compensated financially, and the current residents who had occupied the land were awarded long-term leases. The two cases are illustrative of historical land restitution and identify enabling conditions for the effective functioning of land restitution mechanisms in different contexts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0220.007
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.224
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2019
Admission routes4
Has abstractno

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