Revealing the hidden facets of normative assessments: improving the management of major infrastructure projects through engaged scholarship
Bibliographic record
Abstract
Purpose This study focuses on the dynamic relationship between organizational actors and engaged scholars involved in a normative assessment conducted in a public organization managing major projects. Design/methodology/approach We build on a 15-month engaged scholarship experience carried out in the Ministry of Transport of Quebec. We explain and analyze the normative assessment process, using a storytelling approach and vignettes to explore four situated learning moments. Findings This study offers a deeper understanding of how normative assessment is conducted, and how situated and collective learning occur throughout. We find that both organizational actors and researchers learn through this process and synchronize their mutual learning such that researchers actually participate in a larger organizational transformation. Research limitations/implications Like any qualitative endeavor, this research is context-specific. We offer several research avenues to extend the applicability of findings. Practical implications This article could inspire organizations and scholars to collaborate on normative assessment during organizational transformation. This approach is of particular interest in the context of a worldwide pandemic where public and private organizations all have to adapt to new sanitary, economic, technological and social realities. Social implications In a context marked by growing concern for the research-practice gap and the relevance of scholarship, our study illustrates the development of a mutually beneficial collaboration between practitioners and researchers that enhances understanding of complex organizational phenomena and issues. Originality/value This research highlights the relevance of engaged scholarship and supports normative assessment as a social process to generate mutual learning.
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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 | 0.072 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".