Ruptures During IT-enabled Change: A Sensemaking and Imbrication Analysis
Bibliographic record
Abstract
Focusing on imbrication change as conducted by designers during design and development, we undertook a 2.5-year long case study to examine how 10 hospitals configured technology and planned routine changes as they sought to transform their organizations. Our observations revealed a phenomenon we call ruptures, defined as situations that occur due to breakdowns in the process of IT-enabled change. Ruptures allow us to unpack the temporal process of identifying and resolving instances of disruptive problems during IT-enabled change. Our analysis of ruptures in this context expands the theory of imbrication to account for the role of designers. We demonstrate that three imbrication types—historical, envisioned, and realized—exist cognitively during design and influence the design process. Our results demonstrate how problems arise, how dynamic change unfolds through sensemaking, and how fractal, embedded, imbrications complicate the IT-enabled change process.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 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".