Partnering Up: Including Managers as Research Partners in Systematic Reviews
Bibliographic record
Abstract
Systematic reviews of academic research have not impacted management practice as much as many researchers had hoped. Part of the reason is that researchers and managers differ significantly in their knowledge systems—in both what they know and how they know it. Researchers can overcome some of these challenges by including managers as knowledge partners in the research endeavor; however, doing so is rife with challenges. This article seeks to answer, how can researchers and managers navigate the tensions related to differences in their knowledge systems to create more impactful systematic reviews? To answer this question, we embarked on a data-guided journey of the experience of the Network for Business Sustainability, which had undertaken 15 systematic reviews that involved researchers and managers. We interviewed previous participants of the projects, observed different systematic review processes, and collected archival data to learn more about researcher-manager collaborations in the systematic review process. This article offers guidance to researchers in imbricating academic with practical knowledge in the systematic review process.
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 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 | 0.863 | 0.907 |
| Meta-epidemiology (narrow) | 0.004 | 0.013 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.047 | 0.092 |
| Open science | 0.013 | 0.076 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".