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Record W1970752573 · doi:10.1080/14927713.2007.9651378

Use of national forests by Salish‐Kootenai tribal members: Traditional recreation and a legacy of cultural values

2007· article· en· W1970752573 on OpenAlexvenueno aff
Joseph P. Flood, Leo H. McAvoy

Bibliographic record

VenueLeisure/Loisir · 2007
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
FundersNational Park ServiceU.S. Forest Service
KeywordsRecreationGeographyNational forestReservationTemperate rainforestForestryEnvironmental protectionSocioeconomicsPolitical scienceSociologyEcologyEcosystem

Abstract

fetched live from OpenAlex

Abstract This study focused on the Confederated Salish and Kootenai Tribes of the Flat‐head Reservation in Montana. Its purpose was three‐fold: (a) to understand the outdoor recreation activities of the Salish‐Kootenai Tribal members in surrounding national forests, (b) to clarify the significance of these outdoor activities to Salish‐Kootenai Tribal members, and (c) to make recommendations to managers regarding how changes occurring on national forests may impact Tribal members’ use of these lands. The study highlights the historical and contemporary challenges American Indians face regarding their use of lands near their reservations. Interviews were conducted with 60 Salish‐Kootenai Tribal members. Results indicate that Tribal members participate in a number of outdoor activities in national forests, including hunting, fishing, berry and mushroom picking, camping, hiking, and collecting medicinal plants. Their participation in these activities is often negatively impacted by two factors: (a) management operations, and (b) a perception of racist behaviour on the part of both managers and non‐American Indians using national forests. Management recommendations are given to balance the needs of forest visitors while respecting and preserving American Indian culture and values.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.335
Teacher spread0.256 · 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 designObservational
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

Citations16
Published2007
Admission routes1
Has abstractyes

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