Cultivating the Under-Mined: Cross-Case Analysis as Knowledge Mobilization
Bibliographic record
Abstract
Despite a plethora of case studies in the social sciences, it is the authors' opinion that case studies remain relatively under-mined sources of expertise. Cross-case analysis is a research method that can mobilize knowledge from individual case studies. The authors propose that mobilization of case knowledge occurs when researchers accumulate case knowledge, compare and contrast cases, and in doing so, produce new knowledge. In this article, the authors present theories of how people can learn from sets of cases. Second, existing techniques for cross-case analysis are discussed. Third, considerations that enable researchers to engage in cross-case analysis are suggested. Finally, the authors introduce a novel online database: the Foresee (4C) database. The purpose of the database is to mobilize case knowledge by helping researchers perform cross-case analysis and by creating an online research community that facilitates dialogue and the mobilization of case knowledge. The design of the 4C database is informed by theories of how people learn from case studies and cross-case analysis techniques. We present evidence from case study research that use of the 4C database helps to mobilize previously dormant case study knowledge to foster greater expertise. URN: urn:nbn:de:0114-fqs0801348
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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 | 0.126 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".