Partnering Up: Including Managers as Research Partners in Systematic Reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it