Discussions and Lessons Learned from three iterative and longitudinal studies aiming to optimize the identification and analysis process for stakeholders within a project context
Bibliographic record
Abstract
Project management research has evolved significantly over the past few decades. Traditionally based on positivism and quantitative approaches, work in the field has gradually expanded to include qualitative interpretative approaches (Biedenbach & Muller, 2011). However, the development of new insights seems to have bypassed several key areas within project management, including stakeholder management. Progress relating to this topic could have a theoretical and pragmatic impact. The work of Achterkamp and Vos (2007) and Jepsen and Eskerod (2009), focusing on stakeholders as a key factor in success, has driven interest in this aspect of project management among academics. The result of this data analysis is that researchers have been able to define several observations and questions with the aim of optimizing the complex process discussed by Bourne and Walker (2006).
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 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 | 0.193 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".