Bibliographic record
Abstract
As we finish this book, Canada and the world are responding to the COVID-19 pandemic.To contain the spread of this novel virus, people all over the world are being asked to socially isolate and distance themselves from each other.This moment is an ironic one for us, as these safety requirements are at odds with many of the arguments we have made in this book concerning the success of the neighbourhood-house (NH) model in building social connection in urban communities.To combat COVID-19 through social isolation and social distancing, all NHs in Metro Vancouver have reluctantly closed down their physical sites.These are the exact sites, as we demonstrate in the chapters of this book, where people feel a sense of home and are safe to connect and be connected with friends, neighbours, and strangers.We believe our book shows that NHs have successfully inherited the early settlement-house mission of connecting local residents and bridging them to different organizational stakeholders in the wider community.Facing the challenge of COVID-19, NHs have not forgotten their mission and tradition in this challenging moment of social isolation.They are mobilizing their staff and volunteers to find alternative ways to connect with, and provide supports to, members of the community, particularly the most vulnerable groups, such as seniors who live alone.A dedication to serving the well-being of local residents in Metro Vancouver and building connections in a seemingly fragmented urban context was what first brought us to initiate a systematic investigation of NHs.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.295 | 0.196 |
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".