Yes, but…: Technology, netnography, and futures
Bibliographic record
Abstract
The idea of understanding the emergence of hopeful futures with netnography is no doubt a good one. Although netnography is based on the study of a small group of people, it may help understand where larger groups may be going, based on naturally occurring conversations conducted online. Such public and semi-public discourse tends to be polarized, but the more positive visions may indeed offer hope. In the contrarian view offered here, we temper such optimism with a historical view of prognostication in the realm of consumption and everyday life. We find that the practice of predicting the future has become more quantitative, but no more insightful with the rise of the internet and Big Data analytics. Doing in-depth netnography may, however, help understand how trends form and how they may affect the future as much or more than they predict it. We present a new conceptual understanding of the role of hype and visioneering in creating an atmosphere of excitement toward the latest technological innovation and explain why this is important. • Big Data and marketing analytics offer micro prediction and control but often fail to provide macro understanding. • Netnography offers a deeper, more culturally sensitive, qualitative analysis of social media content. • Social media content may Affect the future as much or more than it predicts it. • Hype cycles for consumer technological innovations are common and create social and traditional media magical excitement. • Visioneering is a technique by which self-fulfilling magical prophesies may be generated by corporations and industries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".