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Record W1505424227

内丽塔·特鲁访谈录(上)

2014· article· zh· W1505424227 on OpenAlexaboutno aff
海伦·史密斯·塔查尔斯基, 郭小苹

Bibliographic record

Venue钢琴艺术 · 2014
Typearticle
Languagezh
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

海:请谈谈你的家庭背景和伴随你成长的家庭生活。你的父母是音乐家吗?特:我妈妈出生在加拿大阿尔伯塔冰岛一个小社区(Icelandic Community in Alberta),是学校老师。6岁时就成了孤儿,我不知道她是如何想办法学习钢琴的,但在我和哥哥威斯利(Wesly)开始学习钢琴时,她已基本放弃不弹了。她立志要把我和哥哥培养成伟大的音乐家。她曾挨家挨户地售卖《世界图书百科全书》,只为凑足钱能购买一台留声机,那是一个我所听到并终生难忘的机器,多么美妙神奇!我因此听到了著名英国钢琴大师克利福德-柯曾(C1ifford Curzon)演奏的勃拉姆斯《d小调钢琴协奏曲》。我妈妈始终致力于把我们社区的音乐组织办得生龙活虎、有声有色,她还想方设法邀请一些著名和权威的艺术家到我们当地——蒙大拿州博兹曼(Bozeman Montana)小城镇演出,我们非常感激她的良苦用心和辛苦努力,让我们因此能够听到许多激动人心且启发音乐灵感的精彩演出。

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score0.716

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.018
Scholarly communication0.0130.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.250
Teacher spread0.238 · 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 designQualitative
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
Published2014
Admission routes1
Has abstractyes

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