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Knowledge Sharing Barriers Affecting Pharmaceutical Supply Chain Performance

2018· book-chapter· en· W2895238063 on OpenAlexaff
Anirban Ganguly, Debdeep Chatterjee, Asim Talukdar

Bibliographic record

VenueAdvances in logistics, operations, and management science book series · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsConcordia University
Fundersnot available
KeywordsSupply chainKnowledge sharingBusinessRanking (information retrieval)Knowledge managementSupply chain managementMarketingComputer science

Abstract

fetched live from OpenAlex

The pharmaceutical supply chain is one of the most complex supply chains in the world. The primary objective of this chapter is to analyze the role of knowledge sharing barriers in supply chain performance. The chapter will explore significant knowledge sharing barriers that might deter the performance of a pharmaceutical supply chain. This chapter is expected to provide the twofold contribution to the academicians and practitioners. Firstly, it will socialize the importance of knowledge sharing barriers and the role they can play in deterring the performance of a pharmaceutical supply chain, and secondly, the prioritized ranking of the identified knowledge sharing barriers is expected to aid the policymakers and managers to understand the relative importance of the knowledge sharing barriers and design their knowledge management strategies accordingly.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.338
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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