How to take care of the earth: a sociopragmatic analysis of cultural identity and contextualized meaning in Canadian environmentalist discourse
Bibliographic record
Abstract
This thesis uses current sociopragmatic theory to investigate the effects of cultural identity and related contextualizing elements (e.g., knowledge of relevant history) on linguistic meaning in environmentalist discourse, as well as framing theory for an interdisciplinary interpretation and additional support of its findings. More specifically, this entails the application of Acton (2014)’s Sociopragmatic Framework to speech data from a documentary film about environmental racism in Canada, which simultaneously provides an instance of substantiated use and validation for the underutilized framework. In order to test the framework’s hypothesized predictive capabilities, the project additionally includes a short survey designed to probe the perception and interpretation of speaker identity and motivation in correlation with linguistic and contextual variables, based on predictions derived from data analysis with the framework. Survey results indicate mixed potential of and the need for further research on the framework’s predictive capabilities, but clearly demonstrate its immense usefulness and versatility as an analytic tool for applied sociopragmatics. The main analysis illustrates the extensive pragmatic influence of cultural identity on environmentalist discourse, particularly with respect to its role as an effective contextualizing element. The thesis concludes that future research on the topic likely needs to focus more specifically on the effects of individual sociocultural background and ideology.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".