LaDEP: A large database of English pseudo-compounds
Bibliographic record
Abstract
The Large Database of English Pseudo-compounds (LaDEP) contains nearly 7500 English words which mimic, but do not truly possess, a compound morphemic structure. These pseudo-compounds can be parsed into two free morpheme constituents (e.g., car-pet), but neither constituent functions as a morpheme within the overall word structure. The items were manually coded as pseudo-compounds, further coded for features related to their morphological structure (e.g., presence of multiple affixes, as in ruler-ship), and summarized using common psycholinguistic variables (e.g., length, frequency). This paper also presents an example analysis comparing the lexical decision response times between compound words, pseudo-compound words, and monomorphemic words. Pseudo-compounds and monomorphemic words did not differ in response time, and both groups had slower response times than compound words. This analysis replicates the facilitatory effect of compound constituents during lexical processing, and demonstrates the need to emphasize the pseudo-constituent structure of pseudo-compounds to parse their effects. Further applications of LaDEP include both psycholinguistic studies investigating the nature of human word processing or production and educational or clinical settings evaluating the impact of linguistic features on language learning and impairments. Overall, the items within LaDEP provide a varied and representative sample of the population of English pseudo-compounds which may be used to facilitate further research related to morphological decomposition, lexical access, meaning construction, orthographical influences, and much more.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".