Bibliographic record
Abstract
This paper builds upon a core metaphor of scientific methodological diffusion as a specialized form of the marketing of ideas. Using as an illustrative the development and spread of netnography, online ethnography of social media data, this paper explores the nature of the creation, legitimation, adoption, and spread of a new scientific method. Viewing method diffusion as a type of marketing suggests a range of implications. Ideas about the method can be viewed, treated, and managed as a type of ‘brand’. The method is not created in a vacuum but, like a marketed new product, is engineered to satisfy a particular scientific or investigative need, and its success depends on how well it satisfies that need. A particular ‘research-oriented segment’ can be investigated, reached, and deliberately targeted. In this article, I explore how institutional waves of academic, geographic, and pragmatic target research audiences helped to reinforce the adoption of a new scientific approach. The method can be positioned intentionally in a particular methodological category, and as superior to other methods. Once the strategy for marketing the method is intact, the tactics for its spread can be introduced. The ideas for the method and methodology can be brought to their audience in a particular form, with particular attributes, through certain distribution or publication channels, promoted through various means, and offered through for a ‘price’ that encapsulates the difficulty of adopting it. The article explores these ideas about the promulgation of a new method using the development of netnography as an extended case study example.
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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".