Can Digital Activism Change Sustainable Supply Chain Practices in the Agricultural Bioeconomy? Evidence from #Buttergate
Bibliographic record
Abstract
Under what conditions will digital activism elicit a response from industry? What is the nature of that response and how does it impact sustainable supply chain practices? I develop three hypotheses in response to these questions by examining a recent case of digital activism targeted at the use of a controversial bioproduct in the Canadian dairy industry. Drawing on 14 key informant interviews as well as a novel Twitter dataset, I hypothesize that digital activism can elicit a response from industry when it originates with a small number of activists, provided that it also spreads to traditional media. I further hypothesize that industry’s response will be superficial and result in only token changes to sustainable supply chain practices due to the ephemerality and lack of cohesion inherent in some forms of digital activism. These hypotheses lay a foundation for broader cross-sectoral research on how industries respond to digital activism directed at their supply chains and add nuance to ongoing debates about the efficacy of digital activism as a means of changing industry practices.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".