Bridging situated learning theory to the resource‐based view of project management
Bibliographic record
Abstract
Purpose This paper aims to present a high‐level conceptual framework to strengthen the conceptual bridge between project management and workplace learning by applying situated learning theory to project management practice to guide shared learning within and between projects. Design/methodology/approach The paper bridges situated learning theory from the workplace learning literature and the resource‐based view (RBV) of project management from the strategic management literature, using them as lenses to view two learning mechanisms in the project management domain, project reviews and communities of practices. Findings The paper finds that situated learning theory can be applied to project management to highlight processes that enable capability development through shared project learning. Research limitations/implications This paper is conceptual in nature and intended to make a case for empirical research that draws on workplace learning literature which is useful to project management as there remains the challenge of leveraging these perspectives for project management practice. Practical implications The paper believes that situated learning theory offers insights that can be leveraged to make project management environments more effective through improved intra‐project and inter‐project shared learning. Originality/value This paper presents a high‐level conceptual framework to bridge situated learning theory to the RBV of project management. The paper finds that situated learning theory is well suited to contribute to an understanding of shared learning in projects and justifies future research.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".