The discursive legitimation of corporate ecological identity in Chinese sustainability discourse
Bibliographic record
Abstract
Abstract Ecological identity involves all aspects of how individuals or collectives identify themselves with nature. This paper aims to examine the discursive construction of corporate ecological identities in corporate sustainability reports in China and evaluate how these identities are legitimated through the lens of ecolinguistic discourse analysis. Our data was drawn from a collection of English-language sustainability reports of Huawei Technologies Corporation (2016–2020). The findings suggest a mix of ecological identities across all texts, among which stewarding nature dominates and it relates to the belief that humans are obligated to steward nature for the sake of sustainability. These ecological identities are discursively legitimized in terms of defining characteristics, social roles, and community memberships. Innovativeness, leadership and ethicalness are legitimated as the corporation’s dominant characteristics which serve as moral identity standards, allowing further legitimation of the social roles and community memberships that the corporation claims. In the case of social roles, green manufacturing depends on green technologies, and both of them point to the instrumentality and rightness of technology in advancing sustainability. These construals uncover the ecological sustainability in the Chinese cultural context, that is, achieving the harmonious coexistence between humans and nature. In legitimizing community memberships, hierarchical relationships between the corporation and other participants are revealed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".