Validity of Computationally Derived Patterns: An Algorithmic Inference Quality Framework
Bibliographic record
Abstract
Computational algorithms are increasingly being adopted to construct theory within information systems and management disciplines. Computationally derived patterns are core to this type of research. Although there is adequate guidance on validating quantitative, qualitative, and mixed methods research, there is little guidance to assess the validity of computationally derived patterns in the process of theory construction. Therefore, drawing on the mixed method inference quality framework, we devised an algorithmic inference quality framework to assess the inference quality of computationally derived patterns. The algorithmic inference quality assesses how plausible computationally derived patterns are and the interpretations of these patterns by the researcher. Our framework consists of one design quality criteria – algorithmic performance and two explanation quality criteria – algorithmic interpretability and human interpretability. We demonstrated the utility of our framework by applying it to a topic modeling algorithm. We also offer guidance in applying our quality framework.
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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 | 0.222 | 0.597 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".