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Record W7060802388

Shape Stops Story

2007· article· en· W7060802388 on OpenAlexaff

Bibliographic record

VenueeYLS (Yale Law School) · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsColumbia College
Fundersnot available
KeywordsStorytellingNarrativeResistance (ecology)Identity (music)MandateState (computer science)
DOInot available

Abstract

fetched live from OpenAlex

Storytelling and resistance are powerful tools of both lawyering and individual identity, as I argue in this brief essay published in Narrative as part of a dialogue on disability, narrative, and law with Rosemarie Garland-Thompson and Ellen Barton. Garland-Thompson's work shows us the life-affirming potential of storytelling, its role in shaping disability identity, and its role in communicating that identity to the outside world. By contrast, Barton powerfully shows how those same life-affirming narratives can force a certain kind of storytelling, can create a mandate to tell one story and not another. In short, Barton reminds us of the need to respect other kinds of stories.\nThe clinical lawyering pedagogy of Jean Koh-Peters and the late Kathleen Sullivan demonstrated a parallel dialectic. Koh-Peters urged aspiring lawyers to use a storytelling approach as the best way both to empower clients – who often want their stories told in court – and to represent their interests before decision-makers who respond to compelling narratives. Sullivan, by contrast, encouraged a resistance approach to advocacy. She helped her law students see that their clients in a clinic on Advocacy for Parents and Children had been forced to reveal the private details of their lives far more than most Americans, and thus that resisting state intrusion was an important part of the advocate's role.\nUltimately, these perspectives – on law and identity – alert us not only to the importance of telling new stories, and of telling challenging stories, but also to the occasional, yet vital, need to stop the stories. They call our attention to the overlooked moment when identity shapes itself by resisting the demand to tell stories.

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 score0.101
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0130.012
Open science0.0020.012
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.1010.048

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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
Published2007
Admission routes1
Has abstractyes

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