Dictionary of Abstract the Words of the Russian Language: Nouns with High Numerical Measure of Abstractness
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
This article demonstrates an experiment based on one of the possible means of creating a semantic dictionary of abstract words. It also analyzes its first results, lexical units that have shown a high level of abstraction in our enquiry among native speakers. The widening field of researches that study abstract words demands a precise definition of units that can be classified as concrete nouns as opposed to the abstract ones. However, this task is made more difficult by a polysemy and complex semantic structure of abstract words. Ideas of cognitive approach point to the fact that one word can have features of both concrete and abstract units, to a different extent depending on context and individual perception. In this approach, the leading role belongs to the semantic criterion of differentiating between concrete and abstract lexical units. It is suggested that this principle should be taken into account when creating a dictionary of abstract vocabulary. While defining the degree of abstraction of a word, a psychosemantic enquiry of native speakers of Russian can be helpful. Results of such interrogation are described in this article.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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