The Oxford Handbook of Business and the Natural Environment
Bibliographic record
Abstract
Environmental issues now loom large on the social, political, and business agenda. Over the past four decades, corporate environmentalism has emerged and been constantly redefined, from regulatory compliance to more recent management conceptions such as pollution prevention, total quality environmental management, industrial ecology, life cycle analysis, environmental strategy, environmental justice, and, most recently, sustainable development. As a result, understanding the intersection of business activity and environmental protection has become increasingly complex, and there has emerged a focus in academic research on business decision-making, firm behavior, and the protection of the natural environment. This handbook reviews the state of the field as it grows into a mature area of study within management science, its achievements, and its future avenues of research. It brings together original contributions in the field along several lines of enquiry. The first six focus on disciplines as delineated in contemporary business schools: business strategy; policy and non-market strategies; organizational theory and behavior; operations and technology; marketing; and accounting and finance. The seventh section reviews emergent and associated perspectives, whilst a concluding section, written by long-standing leaders in the field, discusses the future outlook for research. Contributors to this volume - Andrew J. Hoffman and Tima Bansal Michael V. Russo and Amy Minto Petra Christmann and Glen Taylor Mike Lenox and Jeffrey G. York George Kassinis Andrew King, Andrea M. Prado and Jorge Rivera David P. Baron and Tom P. Lyon Cary Coglianese and Ryan Anderson Lisa L. Shu and Max H. Bazerman Leigh Plunkett Tost and Kimberly A. Wade-Benzoni Jennifer Howard-Grenville and Stephanie Bertels Michael Lounsbury, Samantha Fairclough and Min-Dong Paul Lee Magali A. Delmas and Michael W. Toffel Klaus Weber and Sara B. Soderstrom Robert D. Klassen and Stephan Vachon James D. Abbey and V. Daniel R. Guide, Jr. Reid Lifset and Frank Boons Nigel P. Melville Debra Scammon and Jenny Mish Andrew Gershoff and Julie R. Irwin Timothy M. Devinney Rob Gray and Irene Herremans Nola Buhr and Rob Gray Charles Cho, Dennis Patten and Robin Roberts Rob Bauer and Jeroen Derwall Jean-Louis Bertrand and Bernard Sinclair-Desgagne Bryan Routledge Krista Bondy and Dirk Matten James E. Post Linda C. Forbes and
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.026 |
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".