Bibliographic record
Abstract
Abstract The application of marketing models in political and public sector contexts is examined. The assumptions in marketing of positive outcomes of (i) rapid responses to consumer concerns, (ii) the extension of choice and customisation in product development, and (iii) the application of market research techniques are considered in turn. This analysis suggests that in the political context, responding rapidly to public opinion is not necessarily a sound reaction; extending choice and customisation of products may not best serve public welfare, and applying market research techniques may not provide for the best system for policy decisions. The features of liberal representative democracy, particularly the role of deliberation, informed assent and accountability, have been neglected. Speed of response has been emphasised to the cost of democratic filters and checks on public opinion; enhanced choice, enabled by mass customisation, presents problems of social fragmentation; and the application of market research is no substitute for political discourse and engagement. Copyright © 2003 Henry Stewart Publications
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".