Bibliographic record
Abstract
LANCHART-korpusset udgøres dels af optagelser indsamlet i forbindelse med dialektologiske og sociolingvistiske projekter i 1960’erne, 1970'erne og 1980'erne, dels af optagelser af samtaler indsamlet af Sprogforandringscentret på Københavns Universitet mellem 2005 og 2015. Geografisk dækker korpusset en række lokaliteter bredt fordelt i Danmark samt danske udvandrersamfund i Argentina, Canada og USA. Korpusset er i TextGrid-format, hvilket muliggør en direkte kobling mellem transskriptionerne og lydoptagelserne samt fleksibel annotation af ord og længere tekstpassager. Korpusset er for nylig blevet relanceret i en ny søgeinfrastruktur baseret på Corpus Workbench (CWB) og den brugervenlige søgegrænseflade Korp, som udover hurtige og fleksible søgninger udmærker sig ved at være open source software der frit kan udvides med ny funktionalitet. Indlæsning af korpusdata i konkordansværktøjer som Korp kræver data i lineært format, hvilket medfører særlige problemstillinger i forhold til samtaledata, hvor der ofte forekommer overlap mellem talerne. I artiklen diskuterer vi disse problemstillinger og præsenterer vores løsning i form af en ny partiturvisning, der viser taledataene med lydsporet synkroniseret til transskriptionen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.411 | 0.320 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".