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.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".