From the nice work to the hard work: “Troubling” community‐based CareMongering during the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract CareMongering is a virtually organized community‐based response to COVID‐19 formed in Canada in March 2020, in response to growing concerns about the pandemic. The goal of CareMongering is to care for community members, particularly those experiencing social exclusion, by organizing groups at a local level to support access to basic necessities, services, and resources (e.g., providing groceries and childcare to frontline workers). Following from feminist calls to “trouble” care, we explore the uncomfortable relations that emerged while practicing CareMongering through a case study of a group in Ontario, Canada. Using semi‐structured interviews with group members and organizers and ethnographic content analysis of Facebook group activity, we examine (1) difficult interactions on the group's public Facebook page, (2) strategies used to moderate the group, and (3) the affective and embodied experiences involved in virtually organizing CareMongering. We illustrate our findings through vignettes of one of the author's experiences as a CareMongering group member and composite narratives of social media interactions. We argue that by enacting critical community care, CareMongering groups have the potential to practice care that goes beyond simply caring for or about community needs to also care with communities. The hard work of critical community care involves an intersectional, reflexive, and relational approach that addresses underlying inequalities and promotes actions aimed toward making structural and collective change.
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 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.066 | 0.047 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".