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Record W2489169437 · doi:10.3726/978-3-653-05167-4

Fremdsprachliche Textkompetenz entwickeln

2012· book· de· W2489169437 on OpenAlexaff
Dagmar Knorr, Antonella Nardi

Bibliographic record

VenuePeter Lang D eBooks · 2012
Typebook
Languagede
FieldArts and Humanities
TopicLinguistic Education and Pedagogy
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Texte in einer fremden Sprache zu schreiben, stellt hohe Anforderungen an Produzenten: Verlangt werden domänen- und kulturspezifische Kenntnisse sowie sprachliche, textbezogene und mediale Kompetenzen, die meistens in institutionellen Erwerbssituationen entwickelt und ausgebaut werden. Dementsprechend sollten all diese Aspekte im Rahmen einer mehrsprachigen Schreibdidaktik berücksichtigt werden. Die Beiträge behandeln das Thema «Textkompetenz» aus theoretischer und praxisbezogener Sicht nach textlinguistischen und funktional-pragmatischen Ansätzen. Sie konzentrieren sich überwiegend auf das Sprachenpaar Deutsch-Italienisch. Untersuchungsgegenstände sind Fragen der Kulturformen in Schrifttexten, des Zusammenspiels verschiedener Textebenen, der Überführung vom rezeptiven zum produktiven Wissen, das wissenschaftliche Schreiben und Vortragen im akademischen Kontext sowie didaktisch-methodologische bzw. berufsbezogene Aspekte der Textkompetenz

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)0.004
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.029
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0070.007
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0290.012

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 head0.072
GPT teacher head0.286
Teacher spread0.215 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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