Ecological modernisation? An ecolinguistic analysis of German, Canadian and European techno-fix approaches to climate change.
Bibliographic record
Abstract
Analyzing how climate change is situated in ‘discourse,’ the socially constituted and constitutive use of language, can tell a great deal about how the human subject sees itself in relation to nature and what problematic elements of the social realities it may consequently reproduce. The discourse analyzed here in a corpus of five publications, two Canadian, one German, and two EU, more precisely exemplifies a discourse of “ecological modernisation” which Jänicke (2008) defines as “systematic eco-innovation and its diffusion” (p. 557). In other words, it is the techno-fix approach of creating advanced technology to solve ecological problems. Two research questions have guided the inquiry: first, how does the way in which Canadian, German, & EU political elites address climate change reflect human beings’ relation to nature? Second, what social and ecological reality may this kind of discourse and its corresponding ethical claims encourage and/or continue to constitute? Drawing from Fairclough’s (2015) critical discourse analytical methods as well as elements from Stibbe’s (2021) ecolinguistic framework, the analysis is organized according to five primary cognitive discourse structures: metaphor, framing, evaluation, salience & erasure. It is shown that the discourse portrays responses to climate change as a wartime journey toward an attainable destination against the conceptualized opponent of “climate change.” Moreover, the corpus discourse frames action and nature through a capitalist lens and prioritizes economic growth and technological advancement while discursively erasing elements of the non-human world. It is argued that the ethically intertwined discourse of ecological modernisation as represented in the corpus promotes a social reality that is ecologically ambivalent, appearing at face value to reconcile environmental problems but with a technological and discursive approach which nonetheless reinforces the exploitative relationship between industrial human society and nature.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".