Bibliographic record
Abstract
Case-study research has been defined by Yin as an in-depth investigation of (contemporary) phenomena in a real-life context, particularly equipped to answer how and why questions (2009: pp. 8–18). Yin and other authors of case studies offer various analytical strategies for studying one of a few cases in depth, ranging from theoretically informed pattern matching (Yin, 2009) to strongly inductive approaches (Stake, 1995). This chapter deals with one specific approach: Causal-Process Tracing (CPT). This methodological approach is particularly well suited to answer ‘why’ and ‘how’ questions because it focuses on the causal conditions, configurations and mechanisms which make a specific outcome possible. It is outcome (Y)-centred, which means that the researcher is interested in the many and complex causes of a specific outcome and not so much in the effects of a specific cause (X). In other words, CPT is geared to answer questions like ‘why did this (Y) happen?’ Furthermore, its aim is to reveal the sequential and situational interplay between causal conditions and mechanisms in order to show in detail how these causal factors generate the outcome of interest. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".