EMERGING DISCOURSE INCUBATOR: Delivering Transformational Change: Aligning Supply Chains and Stakeholders in Non‐Governmental Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments and global corporations increasingly both confront and rely on international non‐governmental organizations ( INGO s) to identify, design, and deliver interventions that prompt transformational change in societies, industries, and supply chains. For INGO s, 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 focal INGO assembles 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, how INGO s’ 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 how INGO s 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 of INGO s.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it