Bibliographic record
Abstract
The Accreditation to Direct Research document represents a means of presenting an inventory of the last years that I have spent as a teacher-researcher, consultant and entrepreneur. Following years spent studying advertising in three countries, in England (BA Hons in advertising, Southampton Institute), Information systems in Canada and France (MBA in IS, Laval University, Quebec and DESS1 in New Media of remote communication and project management, Nantes) and marketing in France (Doctorate in marketing at the University of Nantes and Master of Communication Sciences and Techniques at the University of Avignon), I naturally gravitated towards preparation of a thesis at the confluence of these three subjects, after having written a dissertation in each of these institutions, focused on the same theme: advertising on the Internet for entrepreneurs and SMEs. The subject of my thesis therefore touched on a variable common to these subjects, which is interested in the behavior of the screen user, therefore the consumer. The behavior to be studied was purchasing intention in the context of e-commerce. Color constitutes a variable treated both in the disciplines of “marketing”, “advertising”, and “information systems”, i.e. in the three disciplines studied during my student curriculum. Since color was not treated in 2003 as a subject relating to online user behavior, as was the case in a traditional, offline setting, I oriented the subject of my thesis towards the effects of the color of websites on memorization, with cognitive psychology becoming the subject that mattered most to me. All this evolving around the understanding of consumer behavior, and the workings of e-commerce, from ordering to delivery and by comparing cultures such as in France, China, the United States, Poland or Thailand.
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 | 0.014 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".