The Construction and Practice of the Co-Subject Marketing Discourse——A Study on Correction of the Modern Marketing Myopia
Bibliographic record
Abstract
本文针对当前营销理论和实践存在的＂外形化＂问题,梳理了莱维特提出营销近视症后的营销理论发展进路,得出该近视症并没有得到根治而且有新变异的论断和具有主体中心论特征与弊端的现代营销近视症;认为要根治该近视症,首先应克服＂现代营销者研究现代营销＂以及由此产生的＂营销研究的近视症＂局限,以营销研究者的身份、在跨学科和方法论高度与层面去寻求突破性思路和方法。基于这种认识,本文萃取和应用＂后现代性＂、＂主体间性＂与＂网络嵌入性＂等多学科话语作为矫治这种营销近视症的创新方法,重构了具有＂消解对立、善待他者、与他者网络化互嵌共生＂意蕴的共主体营销新话语,并论述了践行这种营销新话语的方式、机理和愿景。
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".