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Record W269845542 · doi:10.4324/9780429306365-9

Customary and Traditional Knowledge in Canadian National Park Planning and Management: A Process View

2019· book-chapter· en· W269845542 on OpenAlexaboutno aff
R Graham, Robert J. H. Payne

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsNational parkProcess (computing)Environmental resource managementGeographyProcess managementPolitical scienceKnowledge managementBusinessComputer scienceArchaeologyEnvironmental science

Abstract

fetched live from OpenAlex

This chapter describes the assumptions related to the types of formal knowledge needed to establish Canadian national parks and to guide their subsequent planning. It examines customary and indigenous knowledge and evaluates its actual and/or potential impact on scientific information used in park planning. The chapter suggests several non-hierarchical alternatives that Parks might consider to ameliorate relations between management and its publics. Visitor Activity Management Process is a pro-active, flexible and conceptual framework – one whose features facilitate integration of social science information with the development of the systems planning process of the agency and the management plan for a park. Interest in indigenous knowledge and customary users’ environmental knowledge and its relationship to conservation planning and management was once considered a narrow area of scholarship, of interest only to a few anthropologists, ethnographers and cultural geographers.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.617

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0110.026
Scholarly communication0.0150.008
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.040
GPT teacher head0.240
Teacher spread0.200 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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