Bibliographic record
Abstract
It is difficult to overstate the degree of uncertainty during the early days of the coronavirus disease of 2019 (COVID-19) pandemic, as government advice largely emphasized self-quarantine and isolation, stringent hygienic and sanitation practices, implementation of regulations around face masks and shields, and closure of congregate public spaces. This uncertainty was especially true for homelessness service providers, as homelessness is a phenomenon which has historically taken place primarily in public and communal spaces. It is important to consider that data collection among people experiencing homelessness (PEH) has always been a complex endeavor, as these populations can be transient, hard-to-reach, and reluctant to engage with researchers. COVID-19 testing was also severely limited throughout 2020, prior to the development of readily available self-administered tests. Understanding the complete picture of COVID-19 transmission within homeless populations during the early days of the outbreak, is therefore immensely challenging. For these reasons, this study, undertaken in August 2020, seeks to record the impact of the early pandemic period on homelessness service systems from a policy perspective. The value of this perspective is twofold: first, it documents how systems in a wide array of contexts responded to a critical public health crisis and can stand as a record of how systems operated prior to and immediately after the outbreak occurred, providing important historical context for future research; and second, it helps contextualize new and emerging data around the experience of PEH during COVID-19 and its lingering impacts.
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 | 0.005 | 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 | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".