Dynamic Process Model of Brokerage for Systemic Social Impact
Bibliographic record
Abstract
Achieving systemic impact to address complex societal problems calls for structural shifts in underlying socioeconomic arrangements and mobilizing stakeholders from different sectors. Motivated by the question of how disconnected and isolated actors connect together to collectively contribute to transformation in an ecosystem, through a qualitative historical analysis of the case of the largest food security organization in Canada, we examine the role of brokerage organizations toward systemic social impact. Using a process lens, we provide evidence for the evolution of an iungens brokerage organization and illustrate how brokerage organizations dynamically and progressively switch between brokerage roles–mediation and catalysis in order to ensure transfer of capital across the actors while building connections and enriching them for partnerships. Our results bring back mediation mechanisms as crucial to structural solutions in the contexts where already existing arrangements are not sufficient for a collective outcome. We also find that brokers can trigger long-term systemic effects by creating and showcasing a model of brokerage in the system that is replicable by other actors in their absence.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".