Bibliographic record
Abstract
The article is devoted to an acute and insufficiently studied issue—the issue of judgmental discourse. While the business language is considered clichéd and neutral, the cases of direct expression of attitude are attracting more attention nowadays. The article attempts to find out what functions judgmental discourse in general performs, as well as what the role and the functional load of a positive assessment is. The material for the study covers excerpts from the business press with examples of positive assessment. The study was carried out using methods of analysis and synthesis, classification, functional-synergetic analysis served as a private scientific method. The article shows that the perception of discourse is a complex phenomenon, subject to the laws of cognition. A language built linearly acquires multidimensional shades of meaning, being actualised in discourse. Consideration of discourse as a synergistic system of meanings highlights properties uncharacteristic for a linear system. For example, the communicative goal can be achieved through undertaking unexpected speech turns, and the use of implications can increase the rhetorical efficiency of an utterance. The author analyses the three most common speech tactics used in judgmental discourse—praise, compliment/flattery, self-identification—and on their material describes synergistic properties—systemacy, openness, self-organisation, non-linearity and heterogeneity of the semantic system of discourse. The study has shown that a positive assessment in the structure of judgmental discourse is widespread and is realised at various levels of the discourse system. In publicistic business discourse, judgment is used not just to express an attitude; it is a tool for forming an opinion, creating a certain image, expressing the position of a printed medium.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".