Using social network analysis to understand the impact of systems integration efforts: a case study from Thunder Bay
Bibliographic record
Abstract
Over the past two decades Canadian municipalities have seen the emergence of formalised systems-level collaborative approaches to addressing homelessness and housing issues. The implementation of such approaches has been widespread and to some extent standardised by the Canadian (federal) government through the mandated formation of ‘community advisory boards’ (CABs) and their associated ‘Community Entities (CEs) which direct the use of federal homelessness funding’. CABs have significantly affected systems-level strategic planning to address homelessness in urban, rural, and remote areas across the country. These groups have had impact and success, but also face challenges related to effective collaboration and governance. Despite the significant influence of these groups – in directing funding and resources to address homelessness – there is little independent research on these groups, their effectiveness, the relationships that constitute CABs or the degree to which they achieve their stated goals of cross-sectoral integration. Social Network Analysis (SNA) is an approach for understanding networked organizational relationships. It has been used in some limited housing and homelessness scholarship to document the quantitative and qualitative features of networks and for understanding the comparative successes and impacts of these efforts. In the broadest sense, SNA can be described as the investigation of relationships among individuals and/or groups in order to identify and interrogate social structures. In this paper, we utilize a case study approach to explore how SNA might contribute to a better understanding of cross-sectoral network building in a CAB with the aim of enhancing systems-level planning to end homelessness.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".