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Record W1585057816

Grimm's Law as a Response to Functional Asymmetry

2013· article· en· W1585057816 on OpenAlexaff
Harold Paddock

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSyntagmatic analysisPhonologyRedundancy (engineering)LinguisticsGenerative grammarOptimality theoryTheoretical linguisticsPhonological ruleComputer sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Guided by principles such as those of economy and symmetry, both major schools of modern phonology (structuralist and generativist) have tended to distinguish too sharply between the paradigmatic and syntagmatic functions of phonological features. In addition, both types of phonology have tended to undervalue the functions of features which could be classified as having syntagmatic functions only. In structural phonology such features were regarded as merely allophonic; in generative phonology they were classified as purely redundant. Redundancy, however, performs the essential function of insurance in human speech (Fry 1977: 75-89)-synchronically, it offsets all types of what is called NOISE in communication theory (Cherry 1966); diachronically, it permits orderly changes from one phonological stage to another. Redundancy, in other words, helps us solve what Weinreich et al. (1968) called the transition problem in historical linguistics. Finally, the fact is that phonological features normally have both syntagmatic and paradigmatic functions, and that it is not always obvious which of these two functions is primary and which is secondary.

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)0.004
metaresearch head score (Gemma)0.014
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: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.013
Scholarly communication0.0020.007
Open science0.0020.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0210.003

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 head0.013
GPT teacher head0.265
Teacher spread0.251 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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