EMERGING DISCOURSE INCUBATOR: Delivering Transformational Change: Aligning Supply Chains and Stakeholders in Non‐Governmental Organizations
Bibliographic record
Abstract
Governments and global corporations increasingly both confront and rely on international non‐governmental organizations (INGOs) to identify, design, and deliver interventions that prompt transformational change in societies, industries, and supply chains. ForINGOs, transformational change is defined as a fundamental, long‐lasting reframing of a social or industrial system through synergistically altering the knowledge, practices, and relationships of multiple stakeholder groups. With each intervention, the focalINGOassembles its own complex supply chain of nonprofit organizations and for‐profit firms to provide the necessary resources and skills. While prior supply chain management literature provides a good starting point, with some generalizability to the nonprofit sector, this study begins with several key differences to explore how interventions are delivered, and then, howINGOs’ supply chains must be aligned. In doing so, at least three critical factors must be taken into account to improve alignment: stakeholder‐induced uncertainty; supply chain configuration; and supply chain dynamism. By synthesizing these factors with prior literature and emerging anecdotal evidence, tentative frameworks and research questions emerge about howINGOs can better leverage their supply chains, thereby offering a basis for scholars in supply chain management to build a much richer and more nuanced research understanding ofINGOs.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".