Project and processes: a convenient but simplistic dichotomy
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore commonalities and differences between projects and processes, and between project management (PjM) and process management (PcM), with a view to challenge this dichotomic typology, clarify the gray areas in between and propose better ways to classify and manage different endeavors. Design/methodology/approach The research compares different tools and techniques used in both fields, explores the respective literatures and uses various examples to bring out similarities and differences. Findings The current paradigms engender a number of organizational endeavors, which are actually complex processes being managed as projects, using the PjM body of knowledge. Because each instantiation takes a somewhat different form, it is treated as a one-of-a-kind undertaking; whereby many of the opportunities for learning and continuous improvement associated with PcM are lost. A reframing and typology is proposed to clarify the central notions involved. Research limitations/implications The proposed model has not been tested empirically and the authors could not agree on all aspects of the paper, though existing differences are more about degrees, nuances and wording than about the basic findings of the paper. Practical implications The research makes the case that two research and practice communities that are evolving independently have much to gain by adopting a unified model and integrating their respective bodies of knowledge. Practitioners would thus access resources that are better adapted to the management challenges they are facing and gain a sustainable source of strategic advantage. Originality/value The paper challenges long-established paradigms between two distinct research streams. A new typology and classification criteria are proposed.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".