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Record W7068378832

The Lies We Tell

2020· dissertation· en· W7068378832 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2020
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeConsciousnessPower (physics)Accident (philosophy)Meditation
DOInot available

Abstract

fetched live from OpenAlex

The Lies We Tell is a semi-autobiographical novel about a gay man who falls into a coma, written by a gay man who has never been in a coma. The text explores topics including inter- generational relationships, online hookup culture in the mid-2000s, twins, and the nature of memory. The novel follows two protagonists, Dale and Luis, a couple of ten years living in Toronto who are on the brink of a breakup. Following a disastrous dinner party, Luis and Dale drive home (drunk) and get in a car accident (thanks to a deer), and thus begins Luis’s coma, during which he vividly relives — and reflects upon — his final year of high school. The novel is told through present- and past-tense narration in the third-person from both Dale and Luis’s perspectives. Inspired in part by other novels about characters in comas, the text uses multiple levels of dreamlike consciousness in an attempt to breathe new life into a tired literary trope. The characters' narrative arcs explore an array of themes, including a meditation on the lure of middle-class stability for gay men, how sexual abuse is internalized, and the terrifying power of self-deception.

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)0.001
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.009
Scholarly communication0.0080.005
Open science0.0000.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0180.008

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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
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

Citations0
Published2020
Admission routes1
Has abstractyes

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