Storytelling Earth and Body
Bibliographic record
Abstract
According to Sylvia Wynter, we are “a storytelling species”: The capacity to narrate the world might be what we hold most in common as “humans” across diverse geographies. In this article, we weave together Black, Indigenous, and third world and women of color feminist scholarship to ask this question: How can storytelling, as an alternate mode of theorization, help us resituate contemporary planetary crises within longer histories and plural understandings of our relations with earth? We closely read three anticolonial (feminist) scholars whose theories illuminate the relationship of race, gender, and nature: Wynter’s genealogy of humans as storytellers; Lorena Cabnal’s elaboration of cuerpo-territorio (body-territory) and ancestral patriarchy; and Mishuana Goeman’s conceptualization of the body as a meeting place. Anticolonial feminist storytelling alters the spatiotemporal scales through which planetary crises are understood by centering the relationship between body and land. We elaborate how the White, cis male, bourgeois and propertied figure of the human reproduces a story that normalizes the racialization of people and ecologies, gendered domination, and extractivism. Revealing this dominant story to be a fiction of modernity, these scholars open a space of possibility, to tell stories otherwise that reimagine what it means to be human on earth. Storytelling as anticolonial praxis troubles the fixity of racial-colonial violence and reconceives the human, not as a liberal subject or fixed object within colonial capitalism, but as a node within a relational network of human and nonhuman kin.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".