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.
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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 | 0.052 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".