Conceptualizing unexpected events in IT projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unexpected events occur during many IT projects and need to be adequately addressed so that their potentially negative impacts can be mitigated. While various tools and methodologies are available to help IT project teams better manage projects, our knowledge of unexpected events remains limited. To better understand such events, their impacts, and how project teams can respond to them, it is important to first comprehend their nature. As a preliminary step in that direction, the present study conceptualizes unexpected events in IT projects based on a case survey of 50 unexpected events described in 38 published case studies. Our analyses suggest three complementary categorizations of unexpected events based on their source, scope and genesis. Further, based on the premise that different unexpected events are likely to lead to different project team responses and outcomes, we suggest several propositions for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it