Case Study Method and Policy Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Case studies are a good part of the backbone of policy analysis and research. This chapter illustrates case study methodology with a specific example drawn from the author’s current research on Internet governance. Real-world problems are embedded in complex systems, in specific institutions, and are viewed differently by different policy actors. The case study method contributes to policy analysis in two ways. First, it provides a vehicle for fully contextualized problem definition. For example, in dealing with rising crime rates in a given city, the case approach allows the analyst to develop a portrait of crime in that city, for that city, and for that city’s decision makers. Second, case studies can illuminate policy-relevant questions (more as research than analysis) and can eventually inform more practical advice down the road. The chapter reviews the relationship between case study research and the aspirations of more nomothetic (law-like generalizations) social science. To study a case is not to study a unique phenomenon, but one that provides insight into a broader range of phenomena. The author’s example of ICANN illustrates issues pertaining to globalization, global governance, and the internationalization of policy processes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it