Online sustainability claims: lessons from high-scoring B corporations in the Canadian food and beverage sector
Bibliographic record
Abstract
Purpose Sustainability certifications can support green innovation in important consumer sectors such as food and beverage. This research interrogates how certified companies communicate sustainability claims online and whether these practices differ from non-certified counterparts. The purpose of the study is to understand if certification stands to alter online communication about sustainability. Design/methodology/approach A discourse analysis of the websites and social media accounts of three highly-rated Canadian B Corps and three matching non-certified companies inductively identified 5 types of sustainability claims: transparency, brand story, green materials/processes, community engagement and sourcing partnerships. A comparative analysis was used to determine if certification alters corporate sustainability communication practices of firms. Findings The findings indicate that sustainability certifications alter external online sustainability communication. Of the 457 sustainability claims coded in the sample, 67.6% are from certified firms. Attaining certification also alters the areas of communication focus, increasing communication about the socially oriented community engagement dimension, which is often underrepresented. Originality/value The research contributes to the understanding of sustainability communication among privately held small and medium-sized enterprises (SMEs), which are currently underrepresented in the literature. The unique sampling used in this study considers how communication is altered post-certification as a novel way to understand the impacts of sustainability certifications.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 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".