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Record W1813216199

How to Make it Rain: A Practical Analysis of Storytelling Forms

2014· article· en· W1813216199 on OpenAlexfundno aff
Mira Singer

Bibliographic record

VenueVassar Scholarship (Vassar College) · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
FundersRaymond and Beverly Sackler Institute for Biological, Physical and Engineering Sciences, Yale UniversityYork UniversityYale University
KeywordsStorytellingComputer scienceLinguisticsNarrativePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The following is an experiment in practical analysis of storytelling media. The project seeks to explore what insights can be gained about media specificity and adaptation theory\nthrough the process of constructing a story and adapting it into background notes, a short story, a one-act play, and a comic book. A meta paper analyzes the constraints and opportunities afforded by each media as observed through the process of creation and adaptation. The historical notes will go last before the paper to facilitate more surprise in the reading experience. There are endnotes classified into H/N (historical note), A/N (author’s note), P/S (primary source), ED/N (editing notes), AD/N (adaptation note), as well as notation marking notes to do with form (F), observations (O), and revelations (R). These notes mean to comment upon the process of adaptation, delineate the connections to historical sources, and provide a window into the process of creation and revision.

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)0.006
metaresearch head score (Gemma)0.022
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.017
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.064
GPT teacher head0.283
Teacher spread0.218 · 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
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
Published2014
Admission routes1
Has abstractyes

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