Peering below the Surface: Social Mechanisms for Analyzing Interorganizational Information Systems Integration
Bibliographic record
Abstract
In an interorganizational relationships (IOR) context, interorganizational information systems (IOS) need \nto be integrated in order to support collaboration between partners and provide a fuller exploitation of the \nsystems they share. Although research stresses the importance of the two phases of the IOS integration, \nthat is the systems development and the systems diffusion, there is a paucity of studies on the \nmechanisms underlying the integration process and their recursive relationships. Adopting a processual \napproach and drawing on the concept of social mechanisms, we propose a multilevel conceptual \nframework that conjectures about the dimensions of the IOS integration process and the underlying social \nmechanisms that explain the how of the process and the relationships that dynamically link these \ndimensions.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".