Veblen 2.0: Neoliberal Games of Social Capital and the Attention Economy as Conspicuous Consumption
Bibliographic record
Abstract
The purpose of this article will be in reading acts of prosumer behaviour in social networking environments through a Veblenian lens, supported in part by the post-Marxist insights of Guy Debord, especially with respect to the issue of celebrity emulation, conspicuous leisure as constructed by the labour of profile management and promiscuous online interactivity, and acts of status enhancement or aggrandizement. Such a discussion must be set in the current context of the normative frame of neoliberal ideology which champions the values of the entrepreneurial self, devolved competitiveness as a form of - in this case social rather than strictly economic - neo-Darwinism, and the touted virtues of speed and connectivity. Ultimately, it is our hope to link these conspicuous online practices to the ideological framework to demonstrate how prosumption plays an integral role in the quantification of the social economy as expressed as “social capital.” In order to achieve these objectives, strict and operational definitions of prosumption, conspicuity in the Veblenian literature, and neoliberalism will be required. The line between social and economic capital is not a definitive one, and that the behaviours and motives associated with increasing social capital may be weighted more to the individual and influenced by neoliberal values that recode the social as derivative of the economic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".