Cognitive segmentation: Modeling the structure and content of customers' thoughts
Bibliographic record
Abstract
Abstract This paper proposes a cognitive segmentation technique that models both customers' cognitive content and structure. Cognitive segmentation provides a quantitative operationalization of idiographic cognitions that can be compared and integrated across customers to move beyond the in‐depth understanding and wide generalizing trade‐off. In addition, cognitive segmentation utilizes participants' own semantics for eliciting and aggregating cognitions. This method allows researchers to understand content in light of structure, as participants' elicited cognitive contents are further interpreted as a function of the complexity of their cognitive structures. The conceptual foundations from personal construct theory as well as a description of the nine‐step implementation process whereby participants fill out a modified version of Kelly's Repertory Grid and complete Borman's trait implication procedure are provided. An application illustrates how cognitive segmentation can identify and assess the size potential of each customer target as a function of their cognitive content and structure. A discussion of the results and directions for further research are also provided. © 2009 Wiley Periodicals, Inc.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".