Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems.
Bibliographic record
Abstract
Theories are sets of causal relationships between constructs and their proxy indicator variables. Theories are tested and their numerical parameters are estimated using statistical models of latent and observed variables. A considerable amount of theoretical development in Information Systems occurs by theory extension or adaptation. Moreover, researchers are encouraged to reuse existing measurement instruments when possible. As a consequence, there are many cases when a relationship between two variables (latent and/or observed) is re-estimated in a new study with a new sample or in a new context. To aid in cumulative theory building, a re-estimation of parameters should take into account our prior knowledge about their likely values. In this paper, we show how Bayesian statistical models can provide a statistically sound way of incorporating prior knowledge into parameter estimation, allowing researchers to keep a “running tally” of the best estimates of model parameters.
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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 | 0.022 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".