Where the Normal is Crisis: Service Delivery to Underserved Populations during the COVID Pandemic
Bibliographic record
Abstract
Women and children subject to violence. Refugees. The incarcerated and criminalized. The homeless. Ethnic and racialized minorities. When a global pandemic hits populations that are already vulnerable, racialized, marginalized, historically subject to oppression, and underserved, the civil society organizations mandated to serve them need all their ingenuity and resourcefulness to provide support while following public health guidelines. As the COVID‑19 global pandemic forced the closure of many workplaces and the re-direction of public social life, the daily lives of vulnerable people, many already struggling on the margins of society, and those mandated to serve and support them changed shape drastically in some ways, and in other ways, not so much. My main argument is that the pandemic of 2020 and consequent imposed restrictions brought about a moment of difference in how our society treats those who are usually and in “normal” times pushed to the margins, invisible and overlooked. Policy spotlight, propelled by panic and a global public health crisis, shone on them, rendering them sharply visible.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".