Business education and its paradoxes: Linking business and biodiversity through critical pedagogy curriculum
Bibliographic record
Abstract
Abstract The Kunming‐Montreal Global Biodiversity Framework, launched during the United Nations Biodiversity Conference in December 2022, encourages governments, companies and investors to publish data on their nature‐related risks, dependencies and impacts. These disclosures are intended to drive businesses to recognise, manage and mitigate their reliance on ecosystem goods and services. However, there is a ‘biodiversity blind spot’ that is evident for most organisations and business schools. Business education rarely addresses the root causes of biodiversity loss, such as the unsustainable exploitation of natural resources. As the dominant positioning of Education for Sustainable Development Goals (ESDG) presents biodiversity in anthropocentric instrumental terms inadequate for addressing ecosystem decline, we posit that a more progressive and transformative ecocentric education through ecopedagogy and ecoliteracy is needed. Both approaches include the development of critical thinking about degrowth, the circular economy and conventional stakeholder theory to include non‐human stakeholders. Using comparative case studies from Northumbria University, the University of Hong Kong and Amsterdam University of Applied Sciences, we illustrate how business education can be transformed to address biodiversity loss, providing theoretical guidance and practical recommendations to academic practitioners and future business leaders.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".