Bibliographic record
Abstract
For the past 60 years, organisations have increasingly been using projects and management of, by and for projects to achieve their strategic objectives (Morris & Jamieson, 2004; Morris & Geraldi, 2011). Project management (PM) makes an important and significant contribution to value creation globally. However, the ‘glocal’ context in which projects are performed shows increasing volatility, uncertainty, complexity, and ambiguity (‘VUCA’) affecting organisations and the socio-economic environment, within which they operate (Gareis, 2005). Two main dimensions are considered in research: uncertainty (and its two dimensions: volatility and ambiguity), and complexity (Bredillet, 2015). Because action takes place over time, and because the future is unknowable, action is inherently uncertain (Aristotle, 1926, 1357a). Acts involve time, irreversibility, indetermination and contingence, and uncertainty (Sanderson, 2012; Knight, 1921). "We simply do not know" (Keynes, 1937, pp. 113–114). Management situations (here both Practice and Research) are complex systems in the way they involve interdependence and connections between actors, ‘objects’ and the context.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".