Integrating Scientific Research: Theory and Design of Discovering Similar Constructs
Bibliographic record
Abstract
Assessing the similarity of proposed theoretical constructs to each other and those previously known and studied is imperative in theoretical research. In this paper we turn to theories of similarity judgement from cognitive psychology for the understanding of the process of establishing similarity between one or more constructs. Then, guided by these theories, we develop an integrated method for automatic detection of similar constructs. We apply the method to constructs from leading IS journals, a major journal in psychology, and the interdisciplinary overlap between the IS and psychology constructs. Our paper contributes to methodology of research, design science research, behavioral IS research, text mining and information retrieval theory and practice, IS research on ontology alignment and schema matching as well as cognitive theories of similarity in psychology
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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 | 0.129 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".