Bibliographic record
Abstract
This article presents an overview of the complex issue of evaluation in the cultural field, with a particular reference to the bibliography on the subject. The article also contains an articulated proposal for the implementation of benchmarks to measure the activity of opera houses. Section 1 offers an outline of the general features of the bibliography on cultural activity evaluations. There are few works of interest, and these are rather recent. They have been divided into three groups, according to their approaches: evaluation of cultural projects, a managerial approach, and a quantitative benchmarking approach. This last approach is more thoroughly examined in the article. Section 2 presents the results of a research project sponsored by the Institute for Cultural Management of the University of Waterloo (Canada) that produced some interesting benchmarks. Section 3 introduces an articulated proposal for evaluation of lyric theatre activity, with reference to Italian organisation of the field. Sections 3.1, 3.2, and 3.3 contain benchmarks conceived for evaluation of both managerial efficiency and effectiveness. Paragraph 4, in conclusion, expresses the wish that the proposed benchmarks might elicit debate leading to improved indicators that could be of use in the present phase of cultural policy.
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 | 0.045 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".