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Record W7071054304

Report on the 22nd Annual DGfS Meeting in Marburg

2019· article· en· W7071054304 on OpenAlexaboutno aff

Bibliographic record

VenueOPUS (Augsburg University) · 2019
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsnot available
Fundersnot available
KeywordsGermanTheme (computing)ConceptualizationPhoneticsPublicationLinguistic analysis
DOInot available

Abstract

fetched live from OpenAlex

Annual DGfS Meeting in Marburg, GermanyThe DGfS (Deutsche Gesellschaft fr Sprachwissenschaft, German Society for Linguistics) organizes the largest European conference on linguistics.The annual meetings are held towards the end of February/beginning of March each year at varying places in Germany.Twelve workshops on different linguistic aspects, usually six on the main conference theme, six ranging freely from phonetics to text linguistics, offer an opportunity for everyone to present a paper and/or to participate in the discussions.Conference languages are German and English and occasionally French.Information can be found on the website http://coral.lili.uni-bielefeld.de/DGfS/.The 22nd Annual Meeting of the DGfS was held from March 1-3, 2000 in Marburg.The conference theme was "The word -structures and concepts".This year, there were 539 participants from all over the world.About 190 papers were presented by researchers from Europe, the United States and Canada, Asia, Africa, Australia and New Zealand in 12 parallel workshops, covering numerous linguistic areas, including parts of speech, conceptualization and grammaticalization, case, graphemic structures, phonology, word finding in acquisition and aphasia, and others.Papers in the workshops ranged from various aspects of linguistic theory and language modeling to language acquisition and loss.Usually, some of the organizers of a workshop decide to publish the papers.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.187
Teacher spread0.180 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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