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Record W177317599

But Names Won't Necessarily Hurt Me: Considering the Effect of Disparaging Statements on Reputation

2013· article· en· W177317599 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicFreedom of Expression and Defamation
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsReputationStatement (logic)Meaning (existential)Context (archaeology)HarmPlaintiffLawPolitical scienceLaw and economicsSociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

The author proposes a change in how some courts apply the test for defamatory meaning — a change that in her view would help to protect freedom of expression without compromising the protection of reputation or altering the substantive law of defamation.To be defamatory, a statement must tend to harm reputation. However, Canadian case law shows that disparaging statements are often assumed to be defamatory, even when they may have little potential to harm reputation because a right-thinking audience member is unlikely to believe them. The author argues that this is the result of an overly literal approach to ordinary meaning, a disregard for how right-thinking people interpret statements, and a tradition of not adducing evidence of context to prove meaning. Social science evidence shows that a variety of factors — from pre-publication knowledge and opinions to the form in which the words were expressed — can substantially alter an audience’s interpretation of a statement. The approach proposed by the author would require courts to place more emphasis on the entire context of an impugned statement in determining whether the statement would lower a right-thinking person’s estimation of the plaintiff. Although leading more evidence of context would add a degree of complexity, it would not place an undue burden on the parties. Any loss of efficiency would be justified, given the importance of freedom of expression and the fact that the aim of defamation law is to protect reputation, not feelings.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.317
Teacher spread0.307 · 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