"Semantic characteristics of acute and dull pain (on the materials of the mcgill pain questionnaire and the large explanatory dictionary of the Russian language edited by S.A. Kuznetsov)"
Bibliographic record
Abstract
"The problem of adequate verbalization of emotions, sensations and feelings is one of the most important in modern applied linguistics. It is urgent to create a database of units on different linguistic levels intended for such verbalization. It is important in interdisciplinary branches for the languages with different structure, primarily in clinical practice, since the accuracy of the diagnosis depends on the ability to verbalize the sensations. In particular, this applies to people with a high level of alexithymia, having significant difficulties with naming their feelings. Alexithymia is a specific cognitive characteristic of the individual, affecting the ability to perceive sensations, emotions and feelings, to recognize them and adequately verbalize. Alexithymia may be a predictor or be closely related to a number of psychosomatic disorders that need to be prevented in a timely manner to avoid complications, so it is relevant to develop a linguistically valid psychometric diagnostic toolkit that is currently missing for alexithymic patients on the material of Russian language. This article is devoted to lexical units in the Russian language which are used for pain description. The authors analyze descriptors traditionally included in clinical questionnaires for the diagnosis of pain syndromes based on the explanations presented in the Large Russian Explanatory Dictionary of the Russian Language, ed. PP.A.Kuznetsov, analyze them from the point of view of semantics, paying special attention to such important and diagnostically important types of pain as acute and dull, discuss analytical data on results of the survey in patients who had pain complaints conducted to determine the validity of the proposed verbal descriptors."
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".