COVID-19 Reflections of Hyperlocal, Placed-Based Engagement
Bibliographic record
Abstract
The degree to which Universities could nimbly and effectively respond to the impacts of the COVID-19 crisis on their local communities depended upon the structure and orientation of their community engagement infrastructure. Institutions that support a hyperlocal form of place-based engagement were uniquely positioned to harness their extensive place-based organizational networks, intimate knowledge of community assets, and existing paths to leverage institutional resources to work alongside neighbors, residents, organizational leaders, elected officials, elders, youth and families committed to everyone’s thriving through the COVID-19 crises. Hyperlocal, place-based engagement describes an engagement approach within higher education in which a university’s community engagement efforts are focused on a bounded area, such as a neighborhood, within a larger city or metropolitan region, and are aligned with that community’s development goals (Dostilio, Ohmer, McFadden, Mathew, & Finkelstein, 2019). These efforts typically advance two goals: to a) position the institution to partner with, and add value to, community building efforts undertaken by the neighborhood being engaged and b) to enhance and accelerate the institution’s ability to forge mutually-beneficial alliances and mobilize knowledge production. Because these efforts are long-term, they create unique conditions for engagement that proved to accelerate community-campus engagements to address COVID-19 impacts. The Community Engagement Professionals (Dostilio, 2017) who lead hyperlocal engagement activities were particularly crucial to their institution’s COVID-19 community responses. At the time of the pandemic, a group of such professionals had been meeting as a learning community to exchange promising practices of hyperlocal engagement. This article is written from the perspective of these professionals, endeavoring to reflect on how their work to steward hyperlocal approaches was challenged and affirmed through the COVID-19 pandemic.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.047 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".