MétaCan
Menu
Back to cohort
Record W2076370088 · doi:10.1037//0882-7974.15.1.65

Effect of off-target verbosity on communication efficiency in a referential communication task.

2000· article· en· W2076370088 on OpenAlexaff
Tannis Y. Arbuckle, Michiko Nohara-LeClair, Dolores Pushkar

Bibliographic record

VenuePsychology and Aging · 2000
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsPsychologyTask (project management)Focus (optics)Cognitive psychologyInhibitory controlNonverbal communicationCognitionDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

The referential communication task was used to see if high off-target verbosity (OTV), defined as excessive speech that is lacking in focus, negatively affects communication of nonautobiographical information. The task required 1 individual (the director) to communicate descriptions of abstract figures to another (the matcher). Out of 455 adults aged 63 to 93 who were screened for OTV, 27 directors were drawn from each of the top and bottom 15% of the range of OTV scores and 26 directors and all 80 matchers from the middle 50%. High OTV directors were less efficient communicators about the figures and showed poorer inhibitory control but did not intrude personal information in their speech. The implications of the findings for the inhibitory deficit and pragmatic change explanations of OTV are discussed.

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)0.002
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.299
Teacher spread0.288 · 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 designObservational
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

Citations66
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuePsychology and AgingSame topicSpeech and dialogue systemsFrench-language works237,207