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Record W3142385696 · doi:10.1177/1038416220983945

Helping actors improve their career well-being

2021· article· en· W3142385696 on OpenAlexaff
Charles P. Chen, Komila Jagtiani

Bibliographic record

VenueAustralian Journal of Career Development · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVulnerability (computing)Variety (cybernetics)DistressIdentity (music)Psychological interventionPublic relationsPsychologyAnxietyCareer counselingSocial psychologySociologyPolitical scienceApplied psychologyPsychotherapist

Abstract

fetched live from OpenAlex

It is generally assumed that visible actors in the performing arts industry maintain overall wellness despite the knowledge that an actor’s life is often characterized by instability. While an actor’s performance is often critiqued subjectively and critically, the variety of occupational risks associated with an actor’s well-being is less closely examined. Prior research suggests those working within the acting profession experience significant levels of distress. As a result, this article, first, aims to address the issues confronting the actor, in particular, anxiety associated with erratic employment, vulnerability to adverse working conditions, and conflict in identity owing to the impact of acting coupled with the effect of economic insecurity. Second, the paper follows with a consideration of key counselling theories to help strengthen this diverse group’s personal well-being and career prospects. By examining counselling interventions, the application of these theories can allow actors to develop optimally in acting industries worldwide.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.074
GPT teacher head0.241
Teacher spread0.167 · 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 designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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