Community Organizations in the Information Age: A study of community intermediaries in Canada
Bibliographic record
Abstract
The aim of the Community Intermediaries Research Project (CIRP) was to investigate the social challenges and needs addressed by Canadian non-profit community-based organizations, the social and community contexts in which they operate, and the information and services they provide to citizens. These organizations are “community intermediaries” because they act as links between the various levels of government (federal, provincial, and municipal) and citizens, providing social services and information to their clients and communities.The CIRP research team used a case-study, mixed methods approach. In-depth case studies of four types of community organizations in different parts of Canada were performed. These employed: onsite observations; in-depth interviews with managerial and staff members; focus group discussions with staff (paid staff and volunteers) and with clients; and self-directed surveys with the organization’s staff, volunteers and clients. Quantitative analysis produced in-depth community profiles using secondary Statistics Canada census data. Data were also gathered from the various forms of content generated by the organizations to deliver the services and information they provide to their respective clients (web pages, pamphlets, newsletters, etc.).This is an invited re-publication of the CIRP final report .
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.046 | 0.010 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".