More than Management: Organizational Perspectives
Bibliographic record
Abstract
Arts administration literature and research typically emphasize managerial functions and profiles over organizations. Despite the great diversity of cultural organizations, and despite the existence of important and rich contextual elements that could prove to be extremely helpful in uncovering some of the challenges that arts organizations and their managers face, our field has a genetic bias towards questions of management over questions of organization. Budgeting, marketing, and issues related to the characteristics of managers (charisma, leadership, training, etc.) are given precedence over any theorization of cultural organizations. It is as if, in the context of arts management literature, organizations only exist implicitly through the existence of management and arts managers. As a level of analysis in its own right, the organization reveals many of the subtleties of the collective nature and life of arts organizations. Moreover, the organizational level sheds light on many important phenomena, such as the sense of identity, the power dynamics at play, the dynamics of organizational change, and the constraints that are exerted on the institutional environment of arts organizations. These questions are only a small sample of the type of questions that are brought to awareness when we approach arts organizations from lenses that are broader than those recommended by a managerial perspective. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".