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.
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 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 | 0.121 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".