Community Care Ignite Further Grassroots Organizing Possibilities for Long-Term Change: Reflections from the Case of Kapit-Bisig Laban COVID Montreal (Linked Arms in the Struggle Against Covid)
Bibliographic record
Abstract
T This presentation centres around reflections and responses of a local chapter of a national mutual aid network Kapit-Bisig Laban COVID (linked arms in the struggle against COVID) in Montreal, Quebec, Canada.The questions I reflect on are:How and why do we care (and have we always cared?) for each other among our communities, in our neighbourhoods, among our kin, and in times of crises when the State again and again shows the holes, gaps, and neglect in social welfare, immigration and health policies and responses?What can we learn from the mutual aid organizing among Filipino and migrant groups that took place during COVID-19 in specific localities, tied to transnational lives and livelihoods?How are mutual aid and grassroots organizing reinforcing and compatible, if they are?What makes mutual aid and care revolutionary?What are we building and how are we building it?How mutual aid and community care ignite furtHer grassroots organizing possibilities for long-term cHange: reflections from tHe case of kapit-bisig laban coVid montreal (linked arms in tHe struggle against coVid)
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.071 | 0.047 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".