Tips for Planning and Implementing Inclusive Programs
Bibliographic record
Abstract
The Tips for Planning and Implementing Inclusive Programs resource is intended to assist those involved in planning programs to support social inclusion. Tips are included for data-driven program development, for outreach to isolated and/or lonely older immigrants and for addressing barriers through a culturally competent lens.\nThis resource is included in the Social Isolation and Loneliness Toolkit, created by the Centre for Elder Research in Oakville ON, Canada. The Toolkit is part of a research project titled “Building Connected Communities: improving Community Supports to Reduce Loneliness and Social Isolation in Immigrants 65+”. The research focused on exploring strategies to effectively reach out to, and support, older immigrants who may be experiencing, or are at risk of experiencing, social isolation and/or loneliness.
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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.096 | 0.038 |
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".