Cognitive segmentation: Modeling the structure and content of customers' thoughts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it