An Empirically Grounded Search for a Typology of Project Management Offices
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
This article uses an empirical contribution to better understand the project management office (PMO). PMOs are an important aspect of project management practice. Their design and management is complicated by the great variability found among PMOs in different organizations. Lack of consensus on their structure and the roles they undertake prevent the establishment of formal standards on PMOs. Having a typology of PMOs can make the great variability much more manageable. However, the typology should be grounded in reality. The aim of this article is to exploit a rich database of descriptions of 500 PMOs to identify patterns in the data that can form the bases for one or more typologies of PMOs. Data on both the organizational context and the characteristics of PMOs were explored. The search for the bases of a typology relies on the identification of statistical associations (1) between the characteristics of PMOs and characteristics of their organizational context, (2) between the different characteristics of PMOs themselves, and (3) between the performance of PMOs and the characteristics of both PMOs and their organizational context. The analysis explores each of these avenues successively in the search for characteristics that are good or poor candidates for forming the basis of a typology of PMOs. The results of the analysis are then integrated into a model.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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