Seeking red herrings in the wood: tending the shared spaces of environmental and feminist geographies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.027 | 0.104 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".