Bibliographic record
Abstract
In recent years several multinational organizations were affected by poor management practices upstream in the supply chain. For example, brands like Joe Fresh, Primark and Bonmarché were all in the hot seat after the collapse of Rana Plaza in Bangladesh in 2013 (Lu 2013; Wieland and Handfield 2013). Increasingly, the general population (the ultimate consumers of products and services) as well as different non-governmental organizations (NGOs) and activist groups are calling for a greater contribution from large corporations to address sustainability issues upstream in their supply chains. When large corporations fail to adequately address (i) environmental damages caused by their first- or second-tier suppliers, (ii) poor working conditions in their contract manufacturers, or (iii) child labour in the extraction or harvest of raw material, they are pitched as bad corporate citizens generating a reputational loss. Such a reputational loss translates into a reduction of goodwill from both market and non-market stakeholders. This chapter focuses on the reputational loss emerging from questionable practices—from a sustainability perspective—in the supply network (‘poor management practices’ hereafter). In fact, a study suggests that the potential negative impact from a supplier with poor management practices is on average a loss of 12% in market capitalization (Lefevre et al. 2010). Therefore, it is important for large organizations with reputational capital to establish proper management systems to minimize their exposure to poor management practices in the supply network.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".