Bibliographic record
Abstract
Projects still suffer from low project management success rates, mainly due to events that occur during their life-cycles, which cause deviations from their main objectives. That is why, recent studies have begun exploring the concept of resilience in project management. These studies aim to reinforce current project risk management practices and improve the capacity of a project to deal with disruptive events. Therefore, this paper reviews first the literature on the concepts of resilience, and of organizational resilience in order to propose a definition of project resilience and to set its dimensions. Second, the development of indicators to assess project resilience is achieved by conducting semi-structured interviews with 10 senior project managers from different industries, in which, 10 case studies were explored and analyzed. As a result, a definition is proposed, and 10 indicators are established to assess two dimensions of project resilience: awareness and adaptive capacity. In future research, these indicators would require a rigorous validation in different project types. This provides project team members with a robust set of indicators with which they would be able to assess their project’s capacity to effectively and efficiently deal with disruptive events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".