Behind Closed Doors: How the Peer-review Process Works and How Arts Councils Make Decisions About Arts Funding
Bibliographic record
Abstract
Visual artists have often expressed discouragement and mistrust of the peer-review process in the arts. In conversations and in interviews I conducted in 2012, visual artists explained that they mistrusted the decision-making granting system because they did not understand art councils’ categorization policies and processes. Once artists were denied funding, they often did not reapply. They wished that they had learned the reason for being turned down. As a visual artist, I share this sentiment. Arts grants applications are assessed under the peer-review model. The peer-review model is a closed-door process. Peer review has become the standard process to assess artistic excellence and artistic merit. Several scholars and artists find that the peer-review process is flawed and hence it has been questioned by the lack of transparency. Hence, this dissertation provides more clarity of the peer-review process and opens the doors in an aim to unveils the process. I study three arts councils: The Canada Council for the Arts (CCA), the Ontario Arts Council (OAC), and the Toronto Arts Council (TAC). This study focuses on the adjudication of funding for the visual arts. In order to understand the complexity of the issues in the art world, I use a multi-disciplinary approach, drawing from education, social psychology, business, economics, and the arts. This study examines the dynamics of the decision-making process in the peer-review process and it is organized in three chapters that resulted in three independent papers that have been submitted for publication. Paper One, using discourse analysis, examines the definition of “artistic excellence” that is applied during the deliberation process. Paper Two studies government priorities that shape arts funding decisions. I draw from Pierre Bourdieu’s concept of Symbolic Power as the mechanism to analyze how government agencies exert influence on arts funding decision. Finally, in Paper Three, I focus on group decision-making. I draw from Irving Janis’ theory of Groupthink, a psychological phenomenon that occurs within a group of people, wherein the desire for conformity in the group results in an irrational or poor decision-making outcome. The three unique research papers explore the overall decision-making dynamic forces of arts councils in regards to peer-review processes and open new doors for the peer-review process because arts funding matters.
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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 | 0.152 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.033 | 0.037 |
| Scholarly communication | 0.053 | 0.032 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".