Introduction: Companies and the Company They Keep: CSR in a ‘Social and Environmental Value Governance Ecosystems’ Context
Bibliographic record
Abstract
The primary purpose of this book is to share the results of a collective exploration by the authors and by several other research project partners into the way in which a wide variety of public, private, and civil society actors impact the process of designing, implementing, and evaluating corporate responsibility in the mining sector of Latin America. By taking a systemic approach that reveals the political economy surrounding the mining industry and its Corporate Social Responsibility (CSR) efforts, we hope to begin to provide a broader view of the myriad contextual relationships and dynamics that can potentially enable or disable the balance of economic, social, and environmental value that CSR strategies pursue. Through this systemic approach, we hope to add a new variant to debates about whether mining is good or bad for communities and countries and whether CSR is good or bad for mining firms and their stakeholders. Rather, by asking how a variety of different social, economic, and political actors negotiate their conflicting interests surrounding large-scale extractive projects, we hope to gain insight into the complex relations bound up in the practices of mining and CSR. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".