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Record W7031795212

Alms & Matter

2016· dissertation· en· W7031795212 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2016
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpider Taxonomy and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQueerRomancePlot (graphics)NarrativeOppressionMidnight
DOInot available

Abstract

fetched live from OpenAlex

Alms & Matter works against the predominance of the tragic mode in queer narratives by taking heteronormative romantic tropes and reapplying them to characters who normally would be offered suffering and strife instead. The novel aims for a queer optimism rooted in a reality that is not, and should not be, limited to tales of queer suffering. The novel’s division into four parts reflects the setting which binds the characters together: a door-to-door fundraising office in Calgary. The protagonists follow a script when soliciting donations, of which Waiting, Rapport, the Ask, and Concern-Handling are fundamental parts. While we follow the characters through their work, we see the main plots go through the same format. The story is set up in Part I, disrupted and developed in Part II, the stakes raised in Part III, and the plot and subplots resolved in Part IV.
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\nSetting the novel in a fundraising office blends oppression with lightheartedness; though the stories the protagonists present are often dire, many of the interactions they have at the door are startling, bizarre, and surreal. Though many of the scenes take place in or around the workplace, and the protagonists talk about contemporary global issues, the plot revolves around the social aspects of their lives. Both of the novel’s primary romantic plots begin with standard tropes of queer suffering, and typically end in tragedy. In Alms & Matter, these tragic tropes are averted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.296
Teacher spread0.265 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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