Integration of non-financial benefits: a systematic review for engaged scholars
Bibliographic record
Abstract
Purpose The complexity of the integration of non-financial benefits (NFB) in major infrastructure projects (MIP) engenders the formulation of networked knowledge between researchers and practitioners. The authors’ research question is as follows: To what extent does scientific knowledge about the integration of NFB into MIP support engaged scholarship or co-construction of knowledge between researchers and practitioners? Design/methodology/approach The paper uses a review of literature published in academic journals on the integration of NFB in MIP. Nearly 300 papers are analysed in depth, based on categories (aspects and sub-aspects) inspired from engaged scholarship and paradoxical participation approaches. The culture of collaboration and the notion of boundary objects are the two main aspects of this categorization of journal papers. Findings First, research on the integration of NFB into MIP is either project-oriented or society-oriented but in a larger proportion for society-oriented. Second, a lot of researches favour an analytic over a holistic approach, despite their openness to dialogue with practitioners through the complexity and conflict. Practical implications It contributes to the theorization of the engaged scholarship. It also provides insights about research avenues to be exploited where these aspects were not sufficiently exploited, as it is often the case with sustainability, for a better collaboration between researchers and practitioners. Linking the culture of collaboration, boundary objects and knowledge co-creation in the engaged scholarship setting encourages a better understanding of the needs (problem to be resolved) of practitioners, by themselves and the researchers. Originality/value The systematic review was conducted in parallel with the organization of two workshops with participants concerned by the integration of NFB into MIP. The paper identified four clusters from their level of compatibility with engaged scholarship.
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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 | 0.052 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.039 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".