Bibliographic record
Abstract
The article investigates the scopes of word semantics. Firstly, the author gives general information about the term concept. The author investigates the thoughts of linguists about the concepts in different languages. For instance, A.Abdullayev writes: “Concepts are inside representatives of the aspects, fragments of the environment in a human’s psychology. We can say they are inside us” (Abdullayev, 2011). N. Chomiski writes: “The concepts that are created in the human’s minds define the form and the meaning of a great number of sentences, and it means that our knowledge and opinions are endless” (Bickerton, 2010). The author underlines the fact that concepts belong to human conscious, and they purely have typically mind characters.Investigating the article we observe that the author stands on the meanings of the wordsespecially on the meanings of the words denoting life and death. Saying literally, a man can be considred to be a walking dictionary created by God. Each of the individuals has its own word stock in its mind. There exist a lot of words with various meanings, and the article deals with the meanings of the words denoting death and life. The author gives their translations both in the English language and in the Azerbaijani language, and it helps us to catch the similar and different meanings that they form inside the contexts. The author comes to the conclusion that the meanings that the people want to express and the meaning that the words express are different. The article gives the list of the meanings of the words suggested by J. Lyons.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.011 | 0.037 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".