Reading the science centre: an interdisciplinary feminist analysis of museum communication
Bibliographic record
Abstract
Science centres are concerned with developing the public understanding of science, and are institutions through which scientific claims about sex and gender are generated and communicated. This interdisciplinary dissertation looks at the process of communicating gender representations in the Ontario Science Centre permanent exhibition A Question of Truth (AQT), in an effort to better understand museums as settings for informal (and potentially transformative) adult learning. AQT is an unusual exhibition that raises questions about scientific objectivity and social discrimination in Western science. The development process used to create AQT was likewise innovative. Exhibit development is rarely documented and published, so my treatment of the five-year development of AQT is also a contribution to the museology literature. This study draws on prior museum learning research, communication theory, feminist standpoint epistemology, and transformative learning theory, and develops a cultural model of museum communication that considers both museum and visitor meaning-making. Data collection techniques included: (1) interpretive content analysis of the exhibition, (2) document analysis on exhibit development files and visitor comment sheets, (3) “intercept” interviews with 18 visitors (and more extensive visitor observations), and (4) long-interviews with five exhibit development team and one advisory panel members. Document analysis and interview findings demonstrate exhibit developers included consideration of gender discrimination as a minor theme throughout the exhibit development process. My reading of the installed exhibition found attention was paid to offer sex-balanced images and voices in the displays. The treatment of sex-based discrimination, however, was largely overlooked in favour of a focus on scientific racism. While most visitors did recognize the main point, describing the exhibition in terms of its social discrimination theme, most failed to connect the concepts of discrimination and scientific bias, and very few recognized any gender content. A few visitors provided evidence of potential transformative learning. However, the staff involved in developing the exhibition experienced the most significant learning; it required painful reflection on institutional practices and personal identities, and resulted in transformative learning for many of the staff involved. I conclude with some suggestions for future exhibit development strategies, applying my reflexive model of museum and visitor meaning-making.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".