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Record W4250495313 · doi:10.32920/ryerson.14652399.v1

Reconciliation & Indigenous Inclusion In Ontario's Wilderness: An Analysis of Recreational Space in Temagami - n'Daki Menan

2021· preprint· en· W4250495313 on OpenAlexafffundabout
Lara Hintelmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsToronto Metropolitan University
FundersAboriginal Affairs and Northern Development Canada
KeywordsWildernessIndigenousRecreationIdentity (music)Inclusion (mineral)ColonialismDecolonizationMeaning (existential)Gender studiesGeographySociologyEnvironmental ethicsPolitical sciencePoliticsLawAestheticsArchaeologyEcologyEpistemology

Abstract

fetched live from OpenAlex

The Temagami wilderness that we know today is the result of both a cultural and natural phenomenon; the result of a struggle over meaning, identity and land. This paper explores how histories and cultures are reflected in the physical and social landscape of recreational space in Ontario. The primary research question surrounds who has access to Temagami and whose voices are represented. The focus is largely on First Nations visibility and inclusion in Temagami, navigating land use tensions between recreational users, resource extraction, and the Teme-Augama Anishnabai. Merging discourse on wilderness as Canadian identity, settler colonialism, and decolonization, this paper explores the contested nature of the wilderness and identifies opportunities for coexistence and a shared future of mutual respect. This research will contribute to our understanding of cottage country - a unique Ontario identity - reflecting on how First Nations’ identity and values can be represented equally alongside settler society. The goal of this work is to contribute to the discussion on opportunities for decolonization of our wilderness landscapes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0120.009
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.126
GPT teacher head0.274
Teacher spread0.148 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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