Bibliographic record
Abstract
Keynote Speaker: Dr. Grace L. Dillon is an academic and author. She is an Anishinaabe professor in the indigenous nations studies program, in the school of gender, race, and nations, at Portland State University. Dr. Dillon is best known for coining the term indigenous futurism, which is a movement consisting of art, literature, and other forms of media which express indigenous perspectives of the past, present, and future in the context of science fiction and related sub-genres. Dr. Dillon is the editor of walking the clouds: an anthology of indigenous science fiction, which is the first anthology of indigenous science fiction short stories, published by the University of Arizona press in 2012. Join us for our annual Solidarity Town Hall program, an anchor discussion as part of Arabic American National Museum’s theme for Fall 2021 – Spring 2022: Istiqbal al Mustaqbal (Welcoming the Future). This year, the Town Hall is themed Imagining Decolonized Futures, highlighting futurist and sci-fi narratives as we imagine a world without colonial concepts. The Town Hall will feature keynote speaker: Anishinaabe academic and author Grace Dillon; and panelists: British Palestinian fiction writer Selma Dabbagh, multidisciplinary Afrofuturist artist Bryce Detroit, Canadian and Anishinaabe filmmaker Lisa Jackson; with moderator Hina Baloch, leader of the Research & Analytics team at GM. This is a virtual event taking place via Zoom.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.018 |
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".