Aligning Capability with Strategy: Categorizing Projects to do the Right Projects and to do Them Right
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
Organizations that undertake many projects need to identify the types undertaken, and use labels to name them. These labels are attributes that form the basis of a project categorization system. There are two reasons why organizations need to categorize projects. The first is to develop and assign appropriate competencies to undertake projects successfully (do them right). The second is to prioritize projects within an investment portfolio to maximize return on investment (do the right projects). Prior research into project classification, the methodology adopted, and the model developed is described. Two major components of a project classification system, the purposes for classifying projects and the attributes used to classify them, are identified; as well as that attributes can be grouped into larger classes. There are also more complex, multidimensional systems for categorizing projects. Finally, how an organization can implement a categorization system is described. This research of project categorization was funded by the Project Management Institute.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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