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Record W2926884838

If a tree falls, for whom does it make a sound? Multilocality and multivocality in Yukon Forests

2012· article· en· W2926884838 on OpenAlexaffabout
Jodie Asselin

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSound (geography)Tree (set theory)GeologyComputer scienceRemote sensingEnvironmental sciencePhysical geographyGeographyOceanographyMathematics
DOInot available

Abstract

fetched live from OpenAlex

This article explores how forests in the Yukon Territory of Canada are experienced and understood by residents in divergent ways. Through a focus on alternative interpretations and versions of forest history, I explore some of the implications of diverse forest perception for understanding multiple use issues in natural areas, as well as what can be learned about possible forest futures. As both multivocal and multilocal, many such forests have numerous individuals and groups who vie to define and articulate meanings about them. As a consequence, many meanings overlap in a single locality, the result being that a forest is not always the same place for all individuals. It also means that forest labels such as ‘used’ and ‘pristine’ are not necessarily exclusive. In the context of forest planning in the Yukon Territory, I argue that it is necessary to embrace the contested nature of place in order to understand local land values and inter-group relations.

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.015
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.183
GPT teacher head0.516
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 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

Citations1
Published2012
Admission routes2
Has abstractyes

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