Evolving netnography: how brand auto-netnography, a netnographic sensibility, and more-than-human netnography can transform your research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The basis of netnography is rather simple. It is grounded by the principle that the perspective of an embodied, temporally, historically and culturally situated human being with anthropological training is, for purposes relating to identity, language, ritual, imagery, symbolism, subculture and many other elements that require cultural understanding, a far better analyst of people’s contemporary online experience than a disembodied algorithm programmed by statistics and marketing research scientists.1 1. Of course, computer scientists are having their day, currently. And there is no doubt that there are many macro behaviours and precise measurements which are handled far better using statistical methods operating on large decontextualised data sets than they are by human participant-observers. But that really is not the point of netnography, of this paragraph or of this special section. View all notes The fundamental positioning of netnography as a research method, its marketing-oriented point of difference, relevant to digital humanities artists, library and information scientists, sociologists, cultural anthropologists, marketing practitioners and consumer researchers alike, is also rather clear. It is that the knowledge we gain from machine understanding of human experience is often sorely limited, and the ethics of the investigatory situation fraught, no matter how large the data set, how cleverly programmed the machine learning algorithms or how extensive the public surveillance.
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.
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 | 0.041 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it