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Record W2009287484 · doi:10.11139/cj.28.3.662-676

Computer Assisted Reading in German as a Foreign Language, Developing and Testing an NLP-based Application

2011· article· en· W2009287484 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCALICO Journal · 2011
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGermanNatural language processingComputer scienceArtificial intelligenceReading (process)LinguisticsForeign languagePhilosophy

Abstract

fetched live from OpenAlex

QuickAssist, the program presented in this paper, uses natural language processing (NLP) technologies. It places a range of NLP tools at the disposal of learners, intended to enable them to independently read and comprehend a German text of their choice while they extend their vocabulary, learn about different uses of particular words, and study German morphology. Learners can access a dictionary, a thesaurus, a concordancer, request a morphological analysis of complex words and query the German Wikipedia for additional information. Abandoning the idea that the application should assume control of the learning process and provide the learning objects, QuickAssist enables users to work with a German text of their choice that is available in electronic form. The results of an initial user study are presented at the end of this paper.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.917
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.316
Teacher spread0.273 · 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