Benefit as a Standard Unit of Measure for Arts Organizations: A Conceptual Analysis
Bibliographic record
Abstract
Benefit is a commonly used concept for expressing positive outcomes of arts participation. The inherent ambiguity of benefit applied to a broad range of arts activities raises issues for research, decision-making, program design, and evaluation. This article offers a conceptual analysis of benefit as a standard unit of measure for design and evaluation of third sector arts organization services. In this article, we explore the possibilities for a standard unit of measure, called Benefit Unit that works toward dispelling the inherent ambiguities of “benefit” in the current discourse on arts programs and services. Conceptual analysis is applied to existing theories of benefit analysis and transaction theory, to advance a framework for Benefit Unit that offers ease of use, coherence, and wide acceptance. Developed for arts organizations, we see potential for any nonprofit organization seeking to establish appropriate measures of the intangible merits of its services. Our research is aimed at decision makers, policy agents, public administrators, and funders who have interest in improving available tools for measuring outcomes of arts services.
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 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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".