Conductor and Cobbler: Leadership Lessons From Large-Scale Research Partnership Facilitation
Bibliographic record
Abstract
“I was a nurse, and now I’m an academic. Why would they think I know how to manage a website!?” (Colleague, personal communication). Many scholars make the transition from managing their own doctoral research project to larger-scale research involving funders, industry partners, community-based partners, or others. Although often well-prepared for research and theorizing, the leadership work of facilitation, project management, budget management, and other logistical and human aspects can be daunting and unfamiliar. This autoethnographic study examines my own experiences of making this transition, using personal notes and journal entries as data for analysis. By analyzing them through a concept of belonging (Pfaff-Czarnecka, 2011), I consider the lessons learned through a large-scale research project: a community-based project involving the creation of a connected network of rural research hubs, and how these lessons impacted my identity as a scholar and director of a research centre. I also share the process of how autoethnography can be enacted in such a role. The findings have been categorized into two metaphors: Conducting an orchestra without any music, and fitting shoes for diverse feet. These lessons offer ways of understanding the struggles and successes of learning to lead large-scale research projects while honouring the diversity of lived experiences and particularities of context, recognizing the need for all to belong within these complexities.
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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 | 0.064 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".