Organization and the arts: Critical conversations on expanding meanings and understandings of who matters
Bibliographic record
Abstract
In this article, I celebrate and discuss Organization’s contributions to the conversation between organization studies and works of art, such as literary fiction, poetry, film, etc. I take this critical conversation forward by highlighting several ways in which such works of art expand meanings for our research, and I then build on this to call for expanding understandings of “who matters” in our engagement with the arts and in our scholarship. In particular, I argue for the case of works of art from/of/about the Global South as an opportunity to take our critical project forward, tracing the promising but complex contours that one is likely to encounter in traversing this relatively unchartered terrain. I conclude by building on this discussion to suggest doing art and being ourselves through art as a part of our conversation with the arts in the role of academic activists and as a means of resistance to current oppressive systems.
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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 | 0.047 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.050 | 0.177 |
| Scholarly communication | 0.037 | 0.033 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.012 | 0.021 |
| 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".