From NLP to Taxonomy: Identifying and Classifying Key Functionality Concepts of Multi-level Project Planning and Control Systems
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
Analysis of literature and industry practices in applied planning and control systems reveals a notable lack of effective processes and stakeholders' understanding regarding the optimal use of these systems. These gaps underscore the urgent need for a refined understanding and discovery of the underlying concepts of existing systems to address the complex dynamics of the planning and control domain better. Therefore, this study employed a multi-step approach using advanced text-mining techniques and expert validation to address these issues. Sentence-Bidirectional Encoder Representations from Transformers (SBERT) for semantic analysis, hierarchical clustering, and word cloud visualization were applied to classify and validate project planning and control system functionality concepts into coherent clusters. Furthermore, a robust taxonomy of functionality concepts was developed by meticulously analysing the findings as well as considering the domain experts' insights. As a result, 148 project planning and control systems' functionalities were classified into 20 coherent clusters with an average 87% alignment rate. A robust taxonomy of these functionalities was then formulated, emphasizing their importance across various scheduling levels. This taxonomy captures the complexities of project planning and control systems, facilitating informed decision-making and the integration of diverse planning and control systems to handle project complexities. The research significantly contributes to the field by clarifying the core concepts of project planning and control systems, making them more understandable and actionable for project stakeholders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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