Towards autonomic marketing
Bibliographic record
Abstract
This paper explores one of the current innovation waves within computing technology, that of the application of Autonomic Computing (AC) to the marketing domain – termed ―Autonomic Marketing‖, the result being an adaptive, highly effective marketing strategy set to significantly change target marketing and a company’s relationship with customers. Marketing has often been at the forefront of business adoption and utilization of the latest computing technologies and functionality. Indeed, the marketing function is interlinked with technology and has been proactively using the capabilities of new technologies from the earliest databases and mail merge functionality to sophisticated Customer Relationship Management systems and intelligent behavioural marketing systems. The Autonomic Computing paradigm provides a framework in which marketing systems could become self-configuring and context-aware, using a variety of learning and decision-making techniques, providing the potential of even more refined targeting of marketing information to customers. In this paper, we introduce the concept of Autonomic Marketing and outline some of the research issues involved in the implementation of such a system that will, indeed revolutionize the marketing world.
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 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".