Meta-Modeling Housing First: A Theory-Based Synthesis Approach
Bibliographic record
Abstract
Abstract: Research synthesis has become an increasingly popular approach for summarizing primary research. In the past two decades, interest in mixed methods reviews has steadily grown, followed, more recently, by an increased attention to theory-based syntheses. This article advances and illustrates a practical application of meta-modeling—a mixed methods, theory-based synthesis approach. The proposed methodology combines meta-analytic and qualitative comparative techniques in developing a program theory—a meta-model—of how and why a program works. As the article illustrates, meta-modeling provides for a structured and transparent synthesis approach for building program theories across existing studies.
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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 | 0.249 | 0.327 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".