Knowledge Brokering for AI Outsourcing Governance: An Agency and Relational Contract Theory Perspective
Bibliographic record
Abstract
Emerging technology outsourcing offers entrepreneurial opportunities and governance challenges, particularly in AI and machine learning. This study examines the role of knowledge brokering in managing cross-organizational and internal barriers in outsourcing relationships. Guided by the agency and relational contract theory, it explores how knowledge brokering reduces information asymmetry, fosters trust and aligns organizational goals. A qualitative case study of a mid-sized AI service provider operating across Iran, Canada, and Germany, using interviews, archival documents, and field observations, highlights how knowledge brokers enhance client education, build confidence in AI solutions, address data security concerns, and manage internal misalignments. By integrating formal governance mechanisms with trust-based collaboration, this study develops a framework for understanding knowledge brokering’s role in AI outsourcing success and offers practical strategies for managing complex technology partnerships.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".