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Record W4283373368 · doi:10.3167/nc.2022.170203

The Discursive Context of Forest in Land Use Documents

2022· article· en· W4283373368 on OpenAlexaff
Jodie Asselin, Gabriel Asselin, Flavia Egli

Bibliographic record

VenueNature and Culture · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
Fundersnot available
KeywordsAmbiguityContradictionStatus quoPolitical scienceContext (archaeology)LegitimacyEnvironmental resource managementGeographyPoliticsEconomicsEpistemologyLaw

Abstract

fetched live from OpenAlex

The term forest can signify many different physical realities. However, discourse analysis of Irish National and European Union forestry-related documents indicates ambiguity around this term is often cultivated rather than clarified. We argue here that policy language often embraces the multiple potential affordances within the term forest as a means of discursively bridging contradictions between economic and conservation goals. While this technique increases the readability and acceptability of such documents by diverse user groups and government bodies, it mutes the on-the-ground tensions of what forests mean for locals. Moreover, cultivating ambiguity favors the status quo through circumventing points of contradiction and shifting the work of interpretation and application of such documents to those on-the-ground, therefore perpetuating existing power differentials. As forests are central to resource management and responses to climate change, addressing this tendency is crucial to finding meaningful and place-specific environmental solutions.

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)0.000
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 score0.468
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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