Online and Remote Community-Engaged Facilitation: Pedagogical and Ethical Considerations and Commitments
Bibliographic record
Abstract
In the early days of the COVID-19 pandemic, many community-engaged practitioners struggled with how to meaningfully and ethically build, maintain or sustain relationships, partnerships, or community-engaged projects, amidst mass upheaval, loss, and uncertainty. Prior to the pandemic, workshops, meetings or community events happened in community drop-ins, social service organizations, or in neighbourhood meeting places. Due to social distancing restrictions, these physical environments abruptly changed to online meeting and messaging applications, phone, and even postal mail. This drastically impacted how community-engaged practitioners approached their facilitation work with communities. This rapid shift also amplified many ethical complexities, like privacy and confidentiality, equitable access, and safety, for those facilitating workshops or programs in non-profit, community-based and participatory research contexts. This article explores findings from a participatory study on how community-engaged practitioners (i.e., community artists, community facilitators, participatory researchers, and participatory visual methods practitioners) across Canada adapted their facilitation approaches to online or remote platforms in the context of COVID-19. We briefly describe our process of doing participatory research online during a pandemic and share findings on how community-engaged practitioners articulated the ethical commitments they brought to their facilitation practice as well as pedagogical and ethical considerations identified for online or remote (i.e., phone, mail) community-engaged facilitation. We conclude by offering reflections on what might be gleaned about online and remote community-engaged facilitation for the present moment. We hope that this article - and the illustrations enclosed - will serve as a guide for emerging and established community-engaged practitioners to reflect on their ‘how and why’ of facilitation when working with and alongside communities for social change.
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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 | 0.092 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.039 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".