Corporate responsibility development paths in the US forest sector
Bibliographic record
Abstract
In the past two decades, a growing public interest in environmental and social issues has led to intensified pressures on forest industry companies to gain social license to operate (SLO) by meeting often conflicting stakeholder expectations at both the global and local levels. The integration of social and environmental concerns into business operations vis-à-vis corporate responsibility (CR) practices has become an essential step in obtaining SLO. In the global forest industry, the adoption and development of CR practices within a company is likely to be path-dependent, requiring the incorporation of basic sustainability practices prior to successfully implementing more refined ones. In this exploratory study, we use a leading sustainability rating measure, namely the Kinder, Lydenberg and Domini index, to capture the multi-dimensionality of CR and to empirically study the development paths of corporate social performance (CSP) among large US companies. We then apply trajectory analysis based on mixture modelling, which helps to identify the possible CR leaders and laggards, to identify different groups of US forest companies that follow similar CR developmental trajectories. The use of trajectory analysis also facilitates exploring the shapes of the particular trajectories. According to the results, forest companies in the sample can be classified into four trajectories, all of which show consistent progress in the level of CSP between 1991 and 2009. Based on the differences between groups, we further discuss potential areas, which may contribute to gaining and maintaining corporate SLO.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".