Examining sustainable supply chain management via a social‐symbolic work lens: Lessons from Patagonia
Bibliographic record
Abstract
Abstract Patagonia, Inc. is widely recognized as a leading brand in sustainable supply chain management in the garment industry, an industry known for its sustainability shortcomings. This study examines Patagonia's supply chain practices through a social‐symbolic work lens. It describes the intentional material work related to Patagonia's inputs, throughputs, and outputs, and also its complementary relational and discursive work. We argue that Patagonia's relational and discursive work are crucial to understanding how Patagonia (a) pursues sustainable supply chain practices, (b) influences its stakeholders to support sustainability, and (c) has positive spillover effects beyond its primary stakeholders. Incorporating a social‐symbolic work lens draws attention to important theoretical and managerial insights—in particular, the need for a holistic rather than reductionist approach and the merit in creating self‐reinforcing positive feedback loops—which are prone to be overlooked in conventional studies of sustainable supply chain management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".