The spatialities of intersectional thinking: fashioning feminist geographic futures
Bibliographic record
Abstract
In this article, we highlight the inherent spatialities of intersectionality and its pivotal importance for feminist geographic thought. Intersectionality was, at its inception, already a deeply spatial theoretical concept, process and epistemology, particularly when read through careful and serious engagement with Black Feminist Thought and the writings of radical women of color. We do so here, revisiting Cooper, Crenshaw, Collins and other key scholars to demonstrate that the interlocking violence of racism, patriarchy, heteronormativity, and capitalism have always constituted a spatial formation. Drawing on feminist geographic thought from the 1990s onwards, we highlight the influence of intersectional thinking on our discipline particularly concerning how racial, gendered and classed power operates in place and through space. These pieces have inspired and driven our work, and we extend them here, recognizing newer scholarship that extends and enriches feminist geography through a postcolonial intersectionality. We close by arguing that intersectional thinking is indispensable to feminist geography. Working in solidarity, across and through the interrogation of difference, with agreement and discord, we encourage a deeper feminist geographic engagement with intersectional thinkers, contributing to more critical (and hopeful) futures for our scholarship.
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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.105 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".