Vender a si mesmo e a sua verdade: o processo de empresarização do eu dos músicos produtores de conteúdo digital
Bibliographic record
Abstract
In this work, we start from the understanding of the entreprisation process as a totalizing phenomenon that promotes the generalization of the enterprise idea in all tissues of the society serving, amongst other aspects, as a referent utilized by individuals to invent, narrate and shape themselves (SOLÉ, 2004; ABRAHAM, 2006; RODRIGUES, 2019; ROSE; 1996). We understand that, with the intensification given by neoliberal ideology (FOUCAULT, 2004; LAVAL & DARDOT, 2009) and the emphasis on selfishness and individualism as values proper to modernity (ROSE, 1998), the self starts not only to be shaped by these traits, but also by entreprise’s values and assumptions. Thus, through what Rose (1996) calls the genealogy of subjectivity, the self is shaped by the enterprisation process, constituting what we call the enterprisation process of the self. To analyze how this process takes place, we interviewed 7 content creator musicians who work with social networks (Instagram, Youtube and TikTok). Through the analysis of the trajectories and the everyday of these interviewees, we can identify that they are shaped over time by traits and values such as entrepreneurship, self-sales, discipline and guilt for leisure, which are some of the elements identified throughout the analysis. Finally, it is clear that the way they perceive themselves in the current music market is superimposed by several assumptions that refer to the entrerprise logic.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".