Countering erasures, attuning to silences, and broadening horizons: applauding and augmenting Annette Gough’s contributions to environmental education scholarship
Bibliographic record
Abstract
This editorial introduces the Special Issue devoted to a discussion of Annette Gough’s (Citation2024) book, Gender and Environmental Education: Feminist and Other(ed) Perspectives. The Selected Works of Annette Gough. The issue consists of eight response papers alongside two papers by Annette – one a summary of the book that offers a career retrospective, the other her reflection on the responses. In this editorial, I summarize these contributions and pull out a few threads for further examination, including topics, theoretical perspectives, and methodological approaches that continue to be marginalized in the field and strategies that have been used to disrupt such erasures and silences like sharing bibliographies and attending to citational justice. I also discuss the importance of environmental education researchers continuing to engage in interdisciplinary scholarship and in broadening our horizons by reading widely, adjacently, and in non-extractive, relational ways. I also suggest that we need to continue to attend to how research representations and academic publishing enhances or inhibits collective knowledge-building. Following Annette, my hope is that the environmental education research community continues to diversify so that, together, we create conditions for responding well to the polycrisis in which we are immersed.
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 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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".