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Record W2802462264 · doi:10.22024/unikent/03/tm.447

Two Spirit and Queer Indigenous Resurgence through Sci-Fi Futurisms, Doubleweaving, and Historical Re-Imaginings: A Review Essay

2017· review· en· W2802462264 on OpenAlexaff
Smokii Sumac

Bibliographic record

VenueUniversity of Kent · 2017
Typereview
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsTrent University
Fundersnot available
KeywordsQueerIndigenousSociologyGender studiesHistoryAnthropologyEthnology

Abstract

fetched live from OpenAlex

Kisukyukyit, my name is Smokii Sumac and I am a member of the Ktunaxa nation. 1 I am twospirit, which means I carry certain responsibilities within the many communities I am a part of.This term, in my understanding, does not define my sexuality, but is perhaps more closely connected to my gender.While some define this term based on a simplified narrative of both male/female spirits existing in the same body, I believe that we could ask a thousand folks who claim two-spiritedness to define it and we would end up with a thousand different responses.So I can only speak for my own experience.In my life, two-spirit has come to have a spiritual meaning, one that calls on me to be in two places (sometimes at once) while also existing in the "in between."On that note, there are a great many other terms I identify with as well, which are also important to my positionality.I am queer, nonbinary, transmasculine, and a poet.I am a writer, a PhD Candidate, and an instructor of Indigenous literatures and creative writing.I am cat-dad, an auntie, an uncle, a sibling, and a child.I am hyper-aware that even as I write this, my experience of gender is shifting, changing, and growing.I open this way to give you a sense of the person examining the texts at hand, locating myself as two-spirit, queer, and Indigenous as I discuss these two books which hold two-spirit/queer Indigenous stories.I also do this to follow protocols of introducing myself, to you, as we enter the relationship of reviewer/reader.In my introductory Indigenous Literatures class this year, I opened with Love Beyond Body Space and Time: An Indigenous LGBT Sci-fi Anthology edited by Hope Nicholson, and including short stories from Richard Van Camp, Gwen Benaway, Cleo Keahna, and recent Governor General Award Winner, Cherie Dimaline.In the discussion following the assigned readings, a self-identified queer and Indigenous student spoke in a somewhat awestruck manner about this anthology, saying "I can see myself in these stories."I share this because it resonates deeply with why I choose to teach Indigenous literatures.Representation matters.This is argued by many scholars, including Adrienne Keene in her blog on cultural appropriation, Native Appropriations, and Daniel Heath Justice in his forthcoming book Why Indigenous Literatures Matter.It also becomes clear in thousands of tiny moments, like my own experience teaching Love Beyond Body Space and Time, where we can see how and why works that re-imagine two-spirit histories -and perhaps more importantly, futures -allow our students a new space to understand themselves, to see themselves within the texts we are teaching, many for the first time.When my student acknowledged their self-recognition, I nodded and held space for that moment in my classroom, remembering the tears in my own eyes as I finished Richard Van Camp's story from this anthology, "Aliens."With a simple and sweet queer love story, Van Camp opened up a world for me.In this world where so often queer, trans, two-spirit bodies are told we are wrong, disgusting, and worse, Van Camp's story tells us we are beautiful and deserving of love.This alone would be reason for me to recommend the text, though I have to admit it is far from perfect.As one example of this, I find Dimaline's statement that "if you see a White Buffalo in a

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.113
GPT teacher head0.363
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractno

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