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Record W4288084310 · doi:10.4324/9781003030485-1

Introductory Conversation

2022· book-chapter· en· W4288084310 on OpenAlexaboutno aff
Kirby Brown, Stephen A. Ross, Alana Sayers

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsConversationComputer sciencePsychologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Rather than composing a conventional introduction to the volume, we decided instead to offer an informal introductory conversation that outlines the genesis of the collection, some of the central questions that organize it, and our hopes for the contributions it might make to ongoing conversations about Indigeneity, modernity, and literary/cultural production. We also thought it important to reflect on what it meant—and means—to do this kind of intellectual work amid an ongoing global pandemic, widespread movements for racial and social justice, and an intensifying environmental crisis. As we note in the acknowledgements and in the discussion that follows, these contexts have touched every contributor to this volume in one way or another, and we think it important to honor these impacts and to reflect honestly and organically on what it means to do intellectual work and how we do it in ways that acknowledge the full humanity—and relationality—of those involved. Where relevant, we’ve gestured to how specific contributions in the volume speak to these questions and have attempted to situate them within larger critical conversations across modernist studies, Native American and Indigenous Studies, US and Canadian literary and cultural studies, and other fields.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.867
Threshold uncertainty score1.000

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)0.5700.001

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 head0.014
GPT teacher head0.165
Teacher spread0.151 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2022
Admission routes1
Has abstractyes

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