Using social and consumer values to predict market‐place behaviour: questions of congruency
Bibliographic record
Abstract
Abstract Social values and consumption values, although intricately linked, are not exactly the same. Nonetheless, marketers contend that the central premise of social value monitoring is that, if one understands people’s values, one can better predict how they will behave in the market‐place. This paper challenges this assumption because policy analysts and industries are relying on both the consumer and social value profiles at a time when society and the market‐place are undergoing a profound transition. Using Canada as a case study, the general societal values of consumers identified by pollsters are discussed relative to nine consumer values espoused by marketers. This comparative analysis suggests that many of Canadians’ alleged consumer values seem to be in direct conflict with their espoused social values. This conclusion implies that the validity of using social values as a proxy variable or predictor for consumer values needs to be examined by researchers and policy analysts. Also, future dialogue needs to occur about adhering to the convention of monitoring social and consumer values using public opinion polls while not marrying this process with public judgement dialogues. Finally, other countries are urged to examine the situation in their market‐place so as to facilitate cross‐cultural comparative analysis of consumer market‐place values.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".