An Intentional Approach to the Engineering of Knowledge-intensive Service Systems
Bibliographic record
Abstract
This paper presents an Intentional Architectural Framework for developing Knowledge-Intensive Service System Architectures (IAF-KISSA). This framework enables the specification and evaluation of knowledge-intensive service systems (KISS) architectures at the levels of network, performance, engagement, and activities. A chosen architecture then allows designing, developing, and adapting a KISS throughout its lifecycle. This research is motivated by the lack of service systems engineering (SSE) methods specifically created for KISS, despite their economic importance in industrialized economies. Examples of KISS include joint innovation initiatives and IT outsourcing contracts. KISS possess a number of distinctive characteristics, including: the knowledge-intensity of their processes and outputs; the inter-organizational coproduction of outputs, and the multi-stakeholder perspective that drives the evaluation of these systems’ performance. SSE aims to define and discover dynamic relationships among entities in order to plan, design, and adapt services systems to cocreate value [1], [2]. SSE calls for a change in perspective in service engineering, from services as products to services as socio-technical systems where actors and resources are configured to collaboratively create value. An important challenge for the field of SSE is the creation of advanced models, methods, and tools for developing service system architectures. However, current service system architectures typically retain a functional and provider perspective on service systems operations without accounting for KISS characteristics. Using an intentional approach leads to modeling a service system in terms of agents, goals, strategies, and dependencies, thus addressing these concerns by moving from a functional to a strategic level of analysis [4]. Using an intentional approach to architecting KISS is thus ideally suited to their social and behavioral complexity. IAF-KISSA contributes a novel approach for architecting KISS, a type of service system that has hitherto been beyond the scope of SSE. Moreover, given the importance of knowledge for all types of service systems [2], IAF-KISSA could provide an innovative manner in which to architect service systems in general.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".