Peter Grzybek (Hg.). 2007. Contributions to the Science of Text and Language. Word Length Studies and Related Issues (Text, Speech and Technology Series 31). Berlin, Heidelberg: Springer. xii, 352 S
Bibliographic record
Abstract
The majority of the 17 contributions to this volume have their origin in a conference held near Graz in 2002 at the beginning of a project funded by the FWF (Fonds zur Förderung der wissenschaftlichen Forschung [Austrian Research Fund]), whose purpose was to study word length and word length frequencies.Factors influencing word length were to be systematically studied, and the emphasis was also to be on the Slavic languages."Experts from all over Europe were invited […] with […] particular emphasis on the participation of scholars from East European countries whose valuable work continues to remain ignored, be it due to language barriers, or to difficulties in the accessibility of their publications.It is the aim of this volume to contribute to a better mutual exchange of ideas."(p.ix)The volume thus is a convenient presentation, in English, of the state of the art of word length studies in the early years of the current decade.Articles from all the "big names" (including the very biggest,
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.025 |
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".