Theorizing Process Dynamics with Directed Graphs: A Diachronic Analysis of Digital Trace Data
Bibliographic record
Abstract
The growing availability of digital trace data has generated unprecedented opportunities for analyzing, explaining, and predicting the dynamics of process change. While research on process organization studies theorizes about process and change, and research on process mining rigorously measures and models business processes, there has so far been limited research that measures and theorizes about process dynamics. This gap represents an opportunity for new information systems research. This research note lays the foundation for such an endeavor by demonstrating the use of process mining for diachronic analysis of process dynamics. We detail the definitions, assumptions, and mechanics of an approach that is based on representing processes as weighted, directed graphs. Using this representation, we offer a precise definition of process dynamics that focuses attention on describing and measuring changes in process structure over time. We analyze process structure over two years at four dermatology clinics. Our analysis reveals process changes that were invisible to the medical staff in the clinics. This approach offers empirical insights that are relevant to many theoretical perspectives on process dynamics.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".