Case Studies and (Causal-) Process Tracing
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-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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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