Framing Well-Being in Sustainability Annual Reports: A Case Study from the Coffee Industry
Bibliographic record
Abstract
The concept of well-being has become increasingly prominent in corporate discourse, particularly in the context of human resource management and sustainability practices. By employing a combination of both quantitative and qualitative methods, this case study examines the discursive construction of well-being in the sustainability annual reports issued by Lavazza, an Italian coffee manufacturer, over a ten-year period (2014–2023), with particular attention to the construction of legitimation strategies and the positioning of social actors. The main findings show that Lavazza incorporates the concept of well-being into business-oriented frameworks. Within the corpus, well-being is primarily attributed to corporate employees and framed as a moral value. Over time, this strategy of legitimisation-by-moralisation has become more closely integrated with rationalisation, helping to position well-being as a measurable, strategic asset directly linked to the optimisation of corporate performance. Notably, this discourse tends to overlook some of the more vulnerable actors within this complex supply chain system, particularly the farmers and growers, whose backgrounding demands an attentive consideration. This case study contributes to the extensive body of literature on Corporate Sustainability Reporting (CSR) by offering novel linguistic insights on the construction of a timely and multifaceted concept such as well-being in corporate communication.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".