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
Over the last fifteen years, the development of blockchain technologies has attracted a large volume of professional expertise, capital investment and media attention. This burgeoning sector of technology practices has coalesced around a few major initiatives (Bitcoin, Ethereum), but it is still moving at a fast pace and its configuration is evolving. If this sector is marked by a variety of technological protocols, financial arrangements and organizational forms, it is also, we would argue, a site of social effervescence. Parties, meet-ups, and the sorts of informal socializing which gather around events and networks of all kinds function to endow the blockchain sector with the characteristics of what, in cultural analysis, are often called “scenes”. The aim of this special issue is to examine the interest of the notion of scene for the analysis of blockchain practices. We argue that the notion of scene may be mobilized as a useful analytical framework not only for the study of blockchain practices, but for that of technology practices more generally. In this introductory article, we ask the following questions: how can the notion of scene contribute to the understanding of blockchain practices? And what sort of research agenda does the notion point to? In the following sections we first identify some “scenic” components in blockchain phenomena. Then we review how media discourses and academic scholarship have framed these phenomena to show that the scene perspective is undertheorized in the context of technology-related social groupings. Finally, we propose a framework to analyse the main dimensions of blockchain scenes, before presenting the contributions to the special issue. With this special issue, we aim to establish a research agenda around technology scenes at the junction of STS and cultural analysis.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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