Painting a Portrait of Organizational Evaluation Capacity in the Canadian Art Museum sector
Bibliographic record
Abstract
Not only is research on program evaluation practice and capacity in art museums largely absent, but also the actual dimensions of evaluation capacity as they could be observed in these unique professional settings have heretofore neither been conceptualized or defined based on empirical data. This study sought to (a) develop a framework that conceptualized what evaluation capacity might look like in art museums and (b) examine how such capacity manifested itself across the framework’s various dimensions both sector-wide and in those Canadian art museums that were most active in conducting a wide range of research and evaluation studies. A two-phase multiple method qualitative research design was used to address the purposes of this research. Phase One involved conducting an interview study to establish an initial knowledge base on Canadian art museum educators’ program evaluation practices and capacities and test the degree to which the initial conceptual framework that was developed to guide this study could be considered an accurate and complete description of evaluation capacity in the Canadian art museum context. Phase Two involved conducting qualitative case studies of two art museums that, based on the interview findings, were identified as operating at the highest level of capacity for evaluation in the country. The study provided the evidence necessary to finalize the initial conceptual framework and concluded that evaluation capacity in Canadian art museums could be described through six central sub-divided dimensions. The study results likewise both painted a portrait of moderate capacity for evaluation across the sector (with smaller pockets of high capacity) and shed empirical light on the phenomenon of developed capacity in selected Canadian art museums. In, demarcating the dimensions that comprise evaluation capacity in art museums, this research makes a significant theoretical contribution to the evaluation literature. Several key recommendations that outline what could be done to strengthen the evaluation capacity of art museums in Canada, meanwhile, represent the main practical implication of this study. These recommendations are likely to be useful not only to the growing number of art museums seeking to integrate evaluation into their organizational cultures but also to several other sectors and organizational types.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.022 | 0.026 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".