Imagining Otherwise: Conceptualising Sustainability in an Era of Extractivism Through an Agonistic Feminist Lens: A Response to Gendron (2024)
Bibliographic record
Abstract
This article explores the tension between sustainability, accounting and governance from an agonistic pluralist perspective adopting feminism as a methodology. Inspired by a commentary by Gendron (2024, in this issue) on the trustworthiness of science in the light of industry sponsorship of academic institutions, this article questions more deeply the acceptance of an extractive mindset which weaves into the organising of society as it influences what topics to prioritise and what to leave out of the mainstream narrative. The article draws in particular from the work of Mouffe (2000, 2013; see also Laclau and Mouffe, 2014) and Davis (2016). These studies, alongside other work, in particular by Black and Indigenous scholars, seeking to dismantle the oppressive bodies of capitalism, colonialism, racism, and patriarchy, have inspired my own reflections for this article. While these are all complex issues in and of themselves, which need further exploration beyond the rich body of existing literature, discussing them together and in relation to sustainability provides an opportunity to realise the equivalences in the struggles and encourage the formation of solidarity.
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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.097 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".