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Record W7068787818

H(u)ina

2020· article· en· W7068787818 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PoetryEvent (particle physics)Power (physics)NarrativeField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

My Honors Capstone project, H(u)ina, is an homage to my ancestors, my homeland, and to those who have believed in me these past four years. This project began in my Spring quarter of my junior year, and has come to completion in the Spring quarter of my final year, and celebrates the complexities of relation. As an indigenous woman, I learned from young the importance of community, and the power of words. Through poetry, I have found a way to process the world around me – just as my peers and I were challenged to do in our first Honors class, when asked to critically analyze not the situation of stories, but the aspect of individual-community relationships. In this collection, readers can share with me the joys and sorrows, the love and the radical that I have experienced as I’ve grown into the person I am today. Yet, poetry is not just about the writing. Indeed, each piece is its own kind of ecology. This I have learned through the field of anthropology, especially in looking at matters of concern, and surprisingly, the Everglades. As matters of concern outline the idea that no event or entity is separate from those around it, the idea so too explains the relationships between mangrove, taro, other rhizomatic plant systems, and us. Thus, I conclude this collection with a poetic essay on these entanglements with which we engage everyday, both consciously and unconsciously. In this network of poems, readers begin with birth and belonging, and conclude with the notion that there are no clear endings, beginnings, or middles, and thus we experience the true nature of relation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.986

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.246
Teacher spread0.220 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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