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Record W2048627089 · doi:10.1017/s0952675701004067

Dominance effects as transderivational anti-faithfulness

2001· article· en· W2048627089 on OpenAlexaff
John Alderete

Bibliographic record

VenuePhonology · 2001
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOptimality theoryMarkednessAlternation (linguistics)MorphemeLinguisticsConstraint (computer-aided design)MathematicsAffixSet (abstract data type)Computer sciencePhonologyPhilosophy

Abstract

fetched live from OpenAlex

This paper presents a theory of morphophonology based on a development in the theory of faithfulness in Optimality Theory. A new constraint type, anti-faithfulness, is proposed that evaluates a pair of related words and requires an alternation in the shared stem. This constraint type is motivated initially by a set of problems, e.g. morphological deletions, segmental exchanges and non-structure preserving processes, which show that morphophonology must encompass more than markedness–faithfulness interactions. The anti-faithfulness thesis is then applied to accentual processes in which affixes idiosyncratically cause deletion of accent in a neighbouring morpheme. It is argued that anti-faithfulness both motivates the observed deletion and accounts for its properties with principles that are generally available in phonological theory. Anti-faithfulness is then shown to extend naturally to the analysis of other affix-induced alternations, including accent insertions, shifts, and retractions of stress and tone, a result which distinguishes this theory from plausible alternatives.

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)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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
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

Citations207
Published2001
Admission routes1
Has abstractyes

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