On the content and structure of values: Universals or methodolical artefacts ?
Bibliographic record
Abstract
During the past fifteen years, Shalom Schwartz developed and continuously refined a comprehensive theory on the structure of values. One significant feature of his approach is that it does not confine itself to the mere distinction of value types. Rather, building on Guttman's facet approach, the theory specifies a set of dynamic relations among values by referring to mutual compatibilities and conflicts in the pursuit of the motivational concerns that they express. In addition, and more importantly for the present study, Schwartz summarised these dynamic relation in terms of a two-dimensional bipolar structure. It is this structure which we tried to replicate in our study. Other than Schwartz, however, we did not use the 'Schwartz Value Survey' for this purpose. Instead, we applied a short version of Morris' 'Ways to Live' developed by Dempsey and Dukes, the 'Kilmann Insight Test', and McClelland's 'Personal Values Questionnaire' to a sample of N=144 Canadian marketing students. Data were analysed by means of nonmetric multidimensional scaling. Results show that many though not all features of the Schwartz values model could be replicated. Correspondence with and deviations from the hypothesised structure are discussed, considering both conceptual and methodological differences in values assessment.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.083 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".