Language, Culture, and Ecology: An Exploration of Language Ecology in Pragmatics
Bibliographic record
Abstract
This paper discussed the relationship between language, ecology, and culture, and claimed that the study of linguistic communication as pragmatics should not be confined to the traditional context, but should focus on a broader ecological environment. It analyzed the context of practical communication from the perspective of language ecology beginning with the discussion of the ecological crisis in communication and found that language, like plants and animals in nature, needed the support of the external environment with certain “soil fertility”. This paper classified ecological context into two types: internal ecological context (psychological-cognitive context) and external ecological context (natural environment and social environment). Based on this classification, the ecological context of pragmatics was further divided into environment-friendly context, addressee-friendly context, and speaker-friendly ecological context. This paper was an exploratory analysis of language ecology in pragmatics, aiming at helping communicative participants find their ecological niche and adopt appropriate strategies to maintain the ecological balance in pragmatic communication.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".