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Record W2761212648 · doi:10.1075/wll.20.1.05sul

STRESYL

2017· article· en· W2761212648 on OpenAlexaff
Simone Sulpizio, Giacomo Spinelli, Cristina Burani

Bibliographic record

VenueWritten Language & Literacy · 2017
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsWestern University
Fundersnot available
KeywordsSyllableSyllabic verseStress (linguistics)Focus (optics)Relation (database)Security tokenLinguisticsComputer scienceNatural language processingSpeech recognitionDatabasePhilosophy

Abstract

fetched live from OpenAlex

Abstract During the last few decades, empirical research on reading has shown increasing interest in syllable units. More recently, stress assignment has become a particular focus of interest. The relation between syllables and stress, however, has yet to be investigated for Italian. In this paper, we describe a new database, STRESYL, that can help researchers to investigate the relation between syllables and stress in Italian. STRESYL offers type and token measures relating stress information to syllable units, both in terms of syllable forms and syllabic structures.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.185
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.004
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1850.176

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 head0.025
GPT teacher head0.418
Teacher spread0.394 · 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 designNot applicable
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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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