Navigating Personal, Professional, Institutional, and Relational Dimensions of Community-Engaged Research
Bibliographic record
Abstract
As universities around the world face plunging revenues coupled with rising expenses, many argue that today’s post-secondary sector is in crisis (Anderson et al., 2020). In some regions, budgetary challenges are exacerbated by performance-based funding models that place an increased focus on impacting local economics and communities more broadly (e.g., Blue Ribbon Panel on Alberta’s Finances, 2019). In response to growing public, personal, and institutional demands for post-secondary institutions to improve their relevance and impact, increasing numbers of academics are pursuing community-engaged approaches to their research. In this paper, two Canadian researchers provide a collaborative autoethnographic account that reflects on and examines their experiences with meaningful and authentic community-engaged research partnerships. The authors explore themes associated with navigating personal, professional, institutional, and relational dimensions of faculty community engagement. In doing so, they draw on and present a modified version of Wade and Demb’s (2009; Demb & Wade, 2012) faculty engagement model that includes relational factors informed by Bringle and Hatcher’s (2002) theoretical framework of relationships. The results of this collaborative autoethnography have broad implications for the practice of research, including implications for work-life balance, tenure and promotion, how service is recognized/categorized, and institutional ethics review board processes.
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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 | 0.036 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.026 | 0.051 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".