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Record W6987943247

Vender a si mesmo e a sua verdade: o processo de empresarização do eu dos músicos produtores de conteúdo digital

2022· article· en· W6987943247 on OpenAlexaboutno aff

Bibliographic record

VenueUFPEL (Universidade Federal de Pelotas) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative research in health
Canadian institutionsnot available
Fundersnot available
KeywordsSelfishnessIdeologyModernityIndividualismReferentPhenomenonSubjectivityProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.042
GPT teacher head0.365
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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