Adventures in Transdisciplinary Translation: Co-creating and Vetting a Novel Research Agenda on Trading Companies as Sustainability Governance Actors
Bibliographic record
Abstract
Tropical agriculture commodities such as cocoa, coffee, and palm oil are popular ingredients across the globe. Despite their abundance, these crops have geographically few growing regions and are often concentrated in countries in the Global South. In order for products to transform from their raw form to their final consumable form, the commodities undergo significant travel and transformation across supply chains. Along these supply chains, known environmental and socioeconomic challenges are embedded at all stages. Stakeholders along the supply chain have long tried to remedy barriers and seek sustainable practices with little scalable success. But recent studies have realized there needs to be increased research dedicated towards a relatively opaque actor, traders. Traders are actors at the center of supply chains and mainly facilitate the movement of crops from upstream producers to downstream consuming markets, but they also serve as communicators along supply chains. This central position provides traders significant insight vertically across supply chains and horizontally across different crop markets. Still, little is known about how traders use this advantageous position and specialized knowledge to advance sustainability and equity goals. This thesis investigates the identified research gap using the Delphi Method and engages with traders not only as the research target but also as research participants. Working alongside academic and trading practitioners, the overall aim of this thesis is to address barriers preventing traders from operationalizing sustainability and looks at traders' self-perceived roles, responsibilities, and opportunities in furthering sustainability objectives in tropical agriculture supply chains.
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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 | 0.050 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.035 | 0.040 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".