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Record W2343230203

Strategic Communications Management in Arts Organizations

2016· dissertation· en· W2343230203 on OpenAlexaboutno aff
Diana M. Weir

Bibliographic record

VenueMacSphere (McMaster University) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsBusinessArts administrationKnowledge managementPolitical scienceArts in educationComputer science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this thesis is to determine the extent to which strategic communications \n \nis being practiced, and its implications on organizational performance in the Canadian \n \narts sector. This thesis discusses the unique characteristics of not-for-profit charitable \n \nperforming arts organizations within the context of strategic communications. As a \n \nmission-based field, the not-for-profit arts sector operates under the premise that it \n \nmust find a market for its product, instead of finding a product for its market. Building \n \non communication and arts marketing theories, this thesis posits that strategic \n \ncommunications management can contribute to the success of arts organizations and \n \naddress the gap between arts products and its market. In particular, this thesis analyzes \n \nthe following components of strategic communications management, in relation to the \n \npractices of the Canadian arts sector: relationship management/marketing, \n \ninterpersonal relationship values, organization public relationship values, and market \n \norientation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.218
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.009
Scholarly communication0.0100.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.019
GPT teacher head0.195
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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