LADEC: The Large Database of English Compounds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Large Database of English Compounds (LADEC) consists of over 8,000 English words that can be parsed into two constituents that are free morphemes, making it the largest existing database specifically for use in research on compound words. Both monomorphemic (e.g., wheel) and multimorphemic (e.g., teacher) constituents were used. The items were selected from a range of sources, including CELEX, the English Lexicon Project, the British Lexicon Project, the British National Corpus, and Wordnet, and were hand-coded as compounds (e.g., snowball). Participants rated each compound in terms of how predictable its meaning is from its parts, as well as the extent to which each constituent retains its meaning in the compound. In addition, we obtained linguistic characteristics that might influence compound processing (e.g., frequency, family size, and bigram frequency). To show the usefulness of the database in investigating compound processing, we conducted a number of analyses that showed that compound processing is consistently affected by semantic transparency, as well as by many of the other variables included in LADEC. We also showed that the effects of the variables associated with the two constituents are not symmetric. In short, LADEC provides the opportunity for researchers to investigate a number of questions about compounds that have not been possible to investigate in the past, due to the lack of sufficiently large and robust datasets. In addition to directly allowing researchers to test hypotheses using the information included in LADEC, the database will contribute to future compound research by allowing better stimulus selection and matching.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it