Unravelling the project escalation enigma: optimising principal-agent dynamics in IT project management
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
To enhance project success, researchers have tirelessly explored the perplexing phenomenon of project escalation and its detrimental impact on budgets. However, a crucial void remains - the lack of actionable solutions to combat and avert project escalation. This paper seeks to fill the gap by adopting a fresh and innovative approach: applying the agency theory. This research identifies the root causes of project escalation and unveils a new approach to overcome this daunting challenge. The cornerstone of this research lies in establishing an optimal mathematical function and meticulously defining start and stop conditions. Project managers can forecast and exercise control over escalation risks through an ex-ante approach. Such foresight gives project stakeholders the knowledge to discern the opportune moment for project launch and, equally crucial, the conditions that demand immediate cessation to avert the perilous grip of escalation, thereby significantly mitigating risks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it