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Record W2914532715 · doi:10.22215/etd/2016-11480

A Referential Analysis of Fictional Names

2016· dissertation· en· W2914532715 on OpenAlexaffabout
Dylan A. Hurry

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPhilosophy and Theoretical Science
Canadian institutionsCarleton University
Fundersnot available
KeywordsReferentPropositionLinguisticsProper nounPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Do fictional names refer to fictional characters?Realists argue they do, while anti-realists argue they do not.According to referentialism, a proper name contributes its referent to the singular proposition expressed by sentences of which the name is a constituent.Furthermore, referentialists argue that the mechanism via which a name means its bearer is best understood as a causal-historical chain of which subsequent uses of the name are parasitic on some initial use (or baptism).For the anti-realist, fictional names present a problem for referentialism as many sentences, such as "Peter Pan was created by J. M. Barrie," seem to refer and express true singular propositions.However, for realists, such I would like to begin by thanking my thesis supervisor, Eros Corazza, for his useful comments on various drafts of this thesis.I would also like to thank him for his continued emphasis that the "devil is in the detail" when it comes to philosophizing.Furthermore, I wish to thank him for his engaging seminars on various problems in the philosophy of language.They have made me realize that the field is where I feel most at home philosophically.Lastly, I would like to thank him for being both a mentor and a friend.I would also like to thank my undergraduate supervisor Neb Kujundzic for introducing me to both the philosophy of language and Austrian philosophy-Brentano and Meinong in particular.The original topic of my undergraduate honours thesis was reference to the non-existent, but upon realizing the vast amount of literature required to gain an understanding of the issues, the subtleness of the debates, and the intricacies involved, I ended up writing on an entirely unrelated topic in epistemology.Thankfully, I remained interested enough in the topic that I was finally able to return to it during my MA research.A special thanks goes out to Professor Christine Koggel and my peers from the research seminar for their helpful comments on the earliest drafts of this project.The discussion section which followed various in-class presentations allowed many unnoticed leads and/or issues to be brought to my attention.Additionally, I would like to thank my friend Gage Bonner for his comments on various drafts of this thesis and

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0050.013
Scholarly communication0.0070.018
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.019
GPT teacher head0.353
Teacher spread0.334 · 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 designTheoretical or conceptual
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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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Same topicPhilosophy and Theoretical ScienceFrench-language works237,207