A Corpus-Based Study of Heteroglossic Features in 2018 BRICS Talk from the Perspective of Engagement System
Bibliographic record
Abstract
Under the guidance of engagement system, which belongs to appraisal theory, this study analyzes data from 2018 BRICS talk through both quantitative and qualitative approaches. Quantitative approach is used to find out the list, characteristics and frequency of engagement resources of the speakers’ utterance. Qualitative approach is employed to explore what interpersonal meaning it implies and how speakers position their own voices among the multi-voices. The results show that the theme of 2018 BRICS talk is about cooperation, development, unilateralism, protectionism, and opportunity for BRICS. With regard to engagement resources, expansion resources are used much more frequently than contraction resources. Furthermore, most of the speakers prefer entertain resources, especially, median and low value of modality resources. Within contraction sub-system, speakers are more willing to use disclaim rather than proclaim to contract dialogic space because proclaim may present more obvious subjective intervention. The engagement distribution features do present interpersonal meaning of BRICS members. On one hand, speakers tend to open up the dialogic space and welcome different opinions from other BRICS members, which indicates that a harmonious discussion atmosphere is established in 2018 BRICS talk. Moreover, median and low values of modality make it easier for audience to accept their points of view and helpful to establish solidarity with other BRICS members because high value modality is sort of assertive. On the other hand, the 2018 BRICS Talk is about sensitive political issues including protectionism, unilateralism and the 4th industry revolution. Therefore, opinions on those topics unavoidably differ from each other. In such cases, speakers prefer to use disclaim resources to reject others’ opinion so as to make clear their own points of view.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".