Set-Theoretic Methods: Qualitative Comparative Analysis (QCA) for IS Research.
Bibliographic record
Abstract
Information and digital technologies have become tightly interconnected with organizational and environmental elements. This ââ¬Ëfusionââ¬â¢ has created a complex system that often exhibits nonlinear, discontinuous change such that a small adjustment in IT systems can trigger drastic changes in other elements, and eventually the whole socio-technical system can change radically and possibly shift to new equilibriums. In such complex dynamics, the role of IT can be better understood as an element of the whole system rather than as a separate independent variable. Notwithstanding such an increasing need for a holistic systemic perspective, there is still a paucity of IS research that investigates how information and digital technologies effectively work together with organizational and environmental elements to produce the expected outcomes either at the individual, group, organization, or ecosystem level. \\ \\ Recently, qualitative comparative analysis (QCA), a set-theoretic method to build a configurational theory, is drawing increasing attention of researchers to its capability to investigate the complex phenomena. QCA developed by Charles Ragin (1987) integrates the strengths of both case-oriented qualitative methods and variable-oriented quantitative methods, and can be applicable for small, medium, or large data. This workshop will foster discussion about how QCA can help IS researchers build novel, richer theories. \\
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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 | 0.053 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".