The Full SPECTRUM: Developing a Tripartite Partnership between Community, Government and Academia for Collaborative Social Policy Research
Bibliographic record
Abstract
Problem: In Canadian society, public policies guide the development and administration of social services and systems, including the public education system, the justice system, family services, social housing and income support. However, because social services are often planned and implemented in a ‘siloed’ manner, coordination and collaboration across departments, sectors and organisations is sorely lacking. Data and resource constraints may prevent services being evaluated to ensure they meet the needs of the people for whom they are intended. When the needs of individuals are not addressed, the result is poor outcomes and wasted resources across multiple areas.Our Response: In 2018, we formed the SPECTRUM Partnership in response to a recognised need for collaborative cross-sector approaches to strengthening the policies that shape social services and systems in our country. The tripartite SPECTRUM partnership comprises representatives from community organisations, government and academia, and is an entity designed to conduct social policy research and evaluation, incorporating interdisciplinary perspectives and expertise from its members. Guided by community-driven research questions and building on existing data resources, SPECTRUM seeks to address specific knowledge gaps in social programs, services and systems. New research findings are then translated into viable public policy options, in alignment with government priorities, and presented to policy-makers for consideration.Implications: In this practice-based article, we describe the key steps we took to create the SPECTRUM partnership, build our collective capacity for research and evaluation, and transform our research findings into actionable evidence to support sound public policy. We outline four of SPECTRUM’s achievements to date in the hope that the lessons we learned during the development of the partnership may serve as a guide for others aiming to optimise public policy development in a collaborative evidence-based way.
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 | 0.149 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.006 | 0.072 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".