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Record W2188570679 · doi:10.82308/2931

Language, subjectivity, and meaningful change

2010· dissertation· en· W2188570679 on OpenAlexaff
Stephen Peters

Bibliographic record

VenueeScholarship@McGill (McGill) · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicRussian Literature and Bakhtin Studies
Canadian institutionsMcGill University
FundersState University of New YorkUniversity of Minnesota
KeywordsSubjectivityPremiseAgency (philosophy)Meaning (existential)AutonomySociologyEpistemologySubject (documents)LinguisticsPsychologySocial scienceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Basée à la fois sur l'analyse d' uvres de fiction (Chinua Achebe's Things Fall Apart, et Franz Kafka's « A Report to an Academy ») et sur des travaux rédigés par des étudiants de second cycle, cette thèse traite de la relation entre les individus et la langue. Je pars du postulat que les efforts que nous mettons pour tenter de comprendre qui nous sommes et le monde dans lequel nous vivons dépendent des schémas culturels d'usage linguistique que nous acquerrons volontairement ou sommes forcés d'acquérir. Dans trois chapitres indépendants mais interreliés, j'explore l'influence de ce postulat sur la formation de la subjectivité, en portant une attention particulière sur la manière dont nous nous connaissons nous-mêmes, sur notre capacité à faire preuve de réflexion critique et sur notre façon de nous représenter à travers la langue. L'objectif général de cette thèse est d'examiner les possibilités et les limitations du positionnement et de l'autonomie individuelle au sein du discours social. Ensemble, ces trois chapitres suggèrent certaines avenues permettant de conceptualiser la participation du sujet dans le changement social, en identifiant la fonction d'apprentissage de la langue afin de permettre aux individus de s'adapter et de s'intégrer dans des contextes culturels particuliers.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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 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
Published2010
Admission routes1
Has abstractyes

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