TORONTO1^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, mehrere Releases)
Bibliographic record
Abstract
Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well. Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Über diese versionsunabhängige DOI ist die jeweils neueste Version des Datensatzes auffindbar.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.066 |
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".