Corporate sustainability reporting in the apparel industry
Bibliographic record
Abstract
Abstract Purpose The purpose of this paper is to identify the reported indicators in corporate sustainability reports, other documents and the web sites of 14 apparel brands belonging to the Sustainable Apparel Coalition (SAC). Design/methodology/approach A content analysis of the corporate sustainability reports, other documents and web sites of the 14 SAC apparel brands was conducted to identify indicators related to sustainability. Qualitative and quantitative data were collected on all reported sustainability initiatives, actions, and indicators. A normative business model was developed for the categorization of the indicators and a cross-case analysis of the apparel brand's sustainability reporting was conducted. Findings In total, 87 reported corporate sustainability indicators were identified. The study finds that there is a lack of consistency among them. The majority of the indicators dealt with performance in supply-chain sustainability while the least frequently reported indicators addressed business innovation and consumer engagement. Originality/value This paper provides one of the first in-depth reviews of the indicators reported by apparel brands within their web sites and other forms of corporate sustainability reporting.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".