A Corpus-Based Study of Body-Part Terms in Verbal Phraseological Units in English and Albanian
Bibliographic record
Abstract
This paper focuses on the use of body-part terms in verbal phraseological units in English and Albanian. By using parallel texts from two different languages, we are going to recognize the structural, semantic, and stylistic properties of body-part terms as well as their cultural context. By comparing structures involving body terms in one language and their equivalents in another, we aim at showing the differences between these languages in the conceptual patterns and grammaticalization, which seem to be widely certified for this part of the lexicon. 18 items representing the terms for body parts (head, face, eye, brow, ear, nose, tongue, mouth, lip, neck, tooth, hand, leg, knee, heel, shoulder, finger, back) were checked in both English and Albanian and compared semantically based on a corpus of verbal phraseological units retrieved and later selected from 8 literary works in English and 12 in Albanian and 3 idiomatic dictionaries. In the course of our findings, we discovered important similarities and differences between the two languages in the use of body-part terms. In this study, both quantitative and qualitative criteria were taken into account and some cultural nuances were drawn from the interpretation of data.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".