Citation matters: mobilizing the politics of citation toward a practice of ‘conscientious engagement’
Bibliographic record
Abstract
An increasing amount of scholarship in critical, feminist, and anti-racist geographies has recently focused self-reflexively on the topics of exclusion and discrimination within the discipline itself. In this article we contribute to this literature by considering citation as a problematic technology that contributes to the reproduction of the white heteromasculinity of geographical thought and scholarship, despite advances toward more inclusivity in the discipline in recent decades. Yet we also suggest, against citation counting and other related neoliberal technologies that imprecisely approximate measures of impact, influence, and academic excellence, citation thought conscientiously can also be a feminist and anti-racist technology of resistance that demonstrates engagement with those authors and voices we want to carry forward. We argue for a conscientious engagement with the politics of citation as a geographical practice that is mindful of how citational practices can be a tool for either the reification of, or resistance to, unethical hierarchies of knowledge production. We offer practical and conceptual reasons for carefully thinking through the role of citation as a performative embodiment of the reproduction of geographical thought.
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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 | 0.062 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.018 | 0.102 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".