Identifying Generative Mechanisms through Affordances: A Framework for Critical Realist Data Analysis
Bibliographic record
Abstract
Critical realism has attracted increasing attention as an alternative to positivist and interpretive research for explaining contemporary phenomena. There are now several sources for information systems' (IS) scholars providing guidance on conducting critical realist studies. However, the most challenging step of a critical realist data analysis, the identification of causal mechanisms, is still insufficiently described. Identifying mechanisms is challenging. Drawing on the concept of affordances as an analytical construct offers the researcher a tool to identify and analyse mechanisms. We present a stepwise framework for identifying structural components of a mechanism, how these components interact to produce an outcome and contextual influences on this outcome. We illustrate the application of the framework through an example of the identification of IS innovation mechanisms in a case study in the airline industry. In doing so, we argue that the approach offers a methodological tool for identifying generative mechanisms, helping the researcher in conducting a more precise data analysis in empirical research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.150 | 0.158 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".