How do stakeholder groups make sense of sustainability: Analysing differences in the complexity of their cognitive frames
Bibliographic record
Abstract
Abstract Characterizing major sustainability issues as ‘grand challenges’ has led to a call for collaboration among heterogeneous stakeholder groups, not least in multi‐stakeholder initiatives (MSIs). Research into MSIs has made substantial progress in understanding their workings; yet, it is still criticized for remaining undertheorized, echoing a criticism of management studies generally as paying insufficient attention to the micro–macro divide. Hence, we examined differences between stakeholder groups in the complexity of their cognitive frames on the topic of sustainability. We analysed 265 cognitive frames across four stakeholder groups (business, government, NGO, education). Analysing these frames in terms of the two dimensions of cognitive complexity—differentiation and integration—we found statistically significant differences in frame complexity between stakeholder groups. These micro‐level cognitive differences can explain macro‐level problems in stakeholder engagement and communication. Hence, we conclude by discussing the implications of our findings for the enhancement of the effectiveness of MSIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".