Systematically Searching Empirical Literature in the Social Sciences: Results from Two Meta-Analyses Within the Domain of Education
Bibliographic record
Abstract
Introduction. This paper provides an overview of the information retrieval strategy employed for two meta-analyses, conducted by a systematic review team at Concordia University (Montreal, QC, Canada). Both papers draw on standards first articulated by H.M. Cooper and further developed by the Campbell Collaboration, which promote a comprehensive approach to systematically searching an extensive array of resources (bibliographic databases, print resources, citation indices, etc.) in order to locate both published and unpublished research. The goal is to verify if searching comprehensively through multiple resources retrieves studies that are unique, and hence, improve the overall representativeness of a diverse body of literature. We also analyze the sensitivity and specificity of the results by data source. Methods. In order to determine the source sensitivity, we consider percentage of results from each source retrieved for full-text review. In order to determine the source specificity, we derive a percentage from the total number of studies included in the final meta-analysis compared against the overall number of initial results found. Results. Results demonstrate the need to search beyond the subject-specific databases of a particular discipline as unique results can be found in many places. Databases for related disciplines provided 129 unique includes to each meta-analysis, and multidisciplinary databases provided 44 and 99 unique includes for the two meta-analyses in question respectively. Manual search techniques were much more sensitive and specific than electronic searches of databases and yield a higher percentage of final includes. Discussion. The results demonstrate the utility of a comprehensive information retrieval methodology like that proposed by the Campbell Collaboration, which goes beyond the main subject databases to locate the full range of information sources, including grey literature.
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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 | 0.210 | 0.434 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.037 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".