Building humanitarian supply chain relationships: lessons from leading practitioners
Bibliographic record
Abstract
Purpose The purpose of this paper is to advance thought and practice on supply chain relationship building, in the context of humanitarian logistics, drawing on lessons from leading practitioners. Design/methodology/approach The presentations were treated like data, enabling grounded research concerning practitioners. The presentations were recorded, transcribed, vetted, and imported into qualitative software (NVivo8) to facilitate further analysis, which led to testable propositions. Findings Three themes emerged, centered around relationship benefits, challenges, and advice on relationship building. Advice from the practitioners led to 11 propositions. Research limitations/implications While the presentations were treated as interview data, there was no opportunity to probe statements made by the speakers. Also, speakers were the sole representatives for their organizations. Finally, the findings cannot be generalized beyond the types of situations and organizations represented at the conference. Practical implications The propositions represent advice from experienced humanitarian practitioners on building supply chain relationships. Social implications Supply chains are economic entities. They are also social entities. Humanitarian supply chains involve people working together to help other people in need. Originality/value There are few published articles on supply chain relationship building, and only several pieces on humanitarian partnerships or relationships. This paper contributes to the literature in a novel way, by drawing on expert speakers at a humanitarian conference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".