Heterogeneity in focus : creating and using linguistic databases
Bibliographic record
Abstract
The papers in this volume were presented at the workshop Heterogeneity in Linguistic Databases', which took place on July 9, 2004 at the University of Potsdam. The workshop was organized by project D1: Linguistic Database for Information Structure: Annotation and Retrieval', a member project of the SFB 632, a collaborative research center entitled Information Structure: the Linguistic Means for Structuring Utterances, Sentences and Texts'. The workshop brought together both developers and users of linguistic databases from a number of research projects which work on an empirical basis, all of which have to cope with different sorts of heterogeneity: primary linguistic data and annotated information may be heterogeneous, as well as the data structures representing them. The first four papers (by Wagner, Schmidt, Lüdeling, and Witt) address aspects of heterogeneous data from the point of view of database developers; the remaining three papers (by Meyer, Smith, and Teich/Fankhauser) focus on data exploitation by the users.
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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 | 0.038 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".