Why Intersectionality in Fiction Matters
Bibliographic record
Abstract
In lieu of an abstract, here is an excerpt: Indigenous peoples often say that from maewizhah, or time immemorial, we have gazed upon ae-iko-dawo-dunnauk-mishi-geezhik and created stories that are maumikaud-kummik. In other words, throughout our histories, Native peoples have looked to the heavens, pondered the universe, and composed fantastical tales that, translated literally, are “out of this world.” This is the very definition of speculative fiction. To us, storytellers are artists and medicine people who provide mishkiki: medicine, healing, and sometimes even solidarity — or, as we say in Anishinaabemowin, inauwinidiwin, which means collectively becoming a “group walking in a body.” When these creatives place frontline communities and characters at the heart of their stories, readers can challenge themselves to become inauwinidiwin, or the coming together as one body-mind on our beautiful yet beleaguered Mizzu-kummik-quae, or Mother Earth. About the author: Grace L. Dillon is a professor in the Indigenous Nations Studies Program at Portland State University, and is of Anishinaabe and European descent. She edited Walking the Clouds: An Anthology of Indigenous Science Fiction and coined the term Indigenous futurisms.
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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.065 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".