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Seeking red herrings in the wood: tending the shared spaces of environmental and feminist geographies

2007· article· en· W1841034993 on OpenAlexaffvenue
Maureen G. Reed

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsScholarshipSustainabilityConstructiveSociologyBoundary-workPublic relationsWork (physics)Order (exchange)Environmental ethicsPolitical scienceEpistemologySocial scienceEcologyEconomicsLaw

Abstract

fetched live from OpenAlex

In this article I argue the need for feminist and environmental geographers to work more diligently to find, mind and tend the intersections of their research agendas to enrich scholarship and deepen impacts on public policy. Such a project requires us to move beyond an obvious call to acknowledge one another's work and towards the boundaries of our respective fields in order to co‐create ‘boundary objects’ that provide opportunities for mutual exchange, collaboration and learning. Rather than being ‘red herrings’ or diversions from our main research foci, boundary objects bring new insights to taken‐for‐granted concepts. I focus on one example to argue that social sustainability of rural places is better understood by an integrated understanding of what constitutes a ‘worker’ in a forestry community. A redefinition of the worker that draws on insights and interests from both environmental and feminist geographers reveals an underlying gender bias in environmental decision‐making processes and illustrates how the concept of social sustainability has been artificially restricted in practice. Nevertheless, collaborations are never easy. I draw attention to potential challenges of such collaborations that include the need to establish mutually agreeable protocols, joint commitment to constructive, respectful debate and strategies to ensure that research provides meaningful contributions to theory and public policy .

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.188
Teacher spread0.181 · 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.

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

Citations13
Published2007
Admission routes2
Has abstractyes

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