Citational politics in and through animal geographies: interrogating onto-epistemological diversity
Bibliographic record
Abstract
This paper contributes to geographic literature on the effects of inequity in citational practice and politics, focusing in particular on onto-epistemological diversity (or lack thereof) in animal geographies' citational structures. Through a bibliometric analysis of journal articles in Anglophone animal geographies (as a subdiscipline of human geography), we examine the intersections between citational trends, the contours of knowledge in the field and everyday academic lives. Our goal in this paper is to highlight some of the ways in which citational inequities are fueled. Specifically, our analysis shows that within Anglophone animal geographies, citational esteem can accrue through institutional networks and shared onto-epistemologies, which often go along with ethical and political orientations that refrain from explicitly contesting the status-quo of anthropocentrism. We ground our analysis with a reflective discussion of everyday academic practice to understand the multi-scalar dynamics and implications of citational politics and prompt heightened reflexivity among geographers concerning how animal and other geographies are constructed and reproduced - and how these reproductions can be contested.
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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 | 0.034 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.026 | 0.038 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".