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Record W7066267931

Go and Heal Our Kinship System

2021· article· en· W7066267931 on OpenAlexaboutno aff

Bibliographic record

VenuePDXScholar (Portland State University) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismMetisContext (archaeology)Theme (computing)NarrativeKinshipTribute
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.011
GPT teacher head0.211
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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