Recommended Practices for Academics to Initiate and Manage Research Partnerships With Organizations
Bibliographic record
Abstract
Although academics can receive considerable training in selecting appropriate research designs, types of data to collect, and methods for analyzing data, as well as guidance on preparing scholarly manuscripts, there is a dearth of information on how to initiate and manage partnerships with organizations in order to conduct high-quality applied research, particularly when the research is quantitative in nature. In this article, we provide our own experience-based insights and recommendations to help academics more easily (a) initiate a research relationship with senior organizational leadership, (b) decide early whether to pursue or end a research collaboration with an organization, (c) keep the organization engaged during the study, and (d) maintain the relationship with the organization after data collection is complete. This information is proposed as a complement to traditional organizational research methods and as instrumental in the pursuit of research salient to the interests of organizational practitioners.
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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 | 0.124 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.035 |
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".