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Record W6890440029 · doi:10.35111/92vj-wg93

Korean Telephone Conversations Transcripts

2003· dataset· en· W6890440029 on OpenAlexaboutno aff

Bibliographic record

VenueAmericanae (AECID Library) · 2003
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTranscription (linguistics)Telephone numberIdentification (biology)Telephone lineText messagingTelephone callConversation

Abstract

fetched live from OpenAlex

<h3>Introduction</h3><br> Korean Telephone Conversations Transcripts was produced by Linguistic Data Consortium (LDC) catalog number LDC2003T08 and ISBN 1-58563-264-3. <br> The telephone conversations on which these transcripts are based were originally recorded as part of the CALLFRIEND project. The CALLFRIEND Korean telephone speech was collected by Linguistic Data Consortium primarily in support of the Language Identification (LID) project, sponsored by the U.S. Department of Defense. The calls were later transcribed for use in other projects. <br> This publication consists of 100 transcribed telephone conversations in Korean. The corresponding speech is published as <a href="../../../LDC2003S03">Korean Telephone Conversations Speech</a>. The Korean orthographic forms from the 100 trascription files serve as the head-words in the associated <a href="../../../LDC2003L02">Korean Telephone Conversations Lexicon</a>. <br> The recorded conversations are between native speakers of Korean and last up to 30 minutes, of which the transcribed speech covers between 15 to 18 minutes. All speakers were aware that they were being recorded. They were given no guidelines concerning what they should talk about. Once a caller was recruited to participate, he/she was given a free choice of whom to call. Most participants called family members or close friends. All calls originated in either the United States or Canada. <br> <h3>Data</h3><br> There are 100 time aligned text files, totalling approximately 190K words and 25K unique words. <br> All files are in Korean orthography: orthographic Korean characters are in Hangul, encoded in KSC5601 (Wansung) system, also known as EUC-KR or ISO-2022-KR. <br> Please follow this link for a sample transcript: <a href="desc/addenda/LDC2003T08.txt" rel="nofollow">txt</a> | <a href="desc/addenda/LDC2003T08.gif" rel="nofollow">gif</a>. <br> <h3>Updates</h3><br> There are no updates available at this time. </br> Portions © 2003 Trustees of the University of Pennsylvania.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.027

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2003
Admission routes1
Has abstractyes

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