Importance of Tacit Knowledge in Geoscience Brought to the Surface through Artistic Methods
Bibliographic record
Abstract
This study of scientists’ reactions to the experience of an art exhibition, researches Polyani’s (2009) tacit knowing, a knowledge that we cannot easily express into words, and Deleuze and Guattari’s (1987) striated and smooth spaces, with striated being a channelled and restricted way of thinking compared to smooth as free flowing and creative. To research these concepts, a psycho-social method—the Visual Matrix (VM)—is used as a research method. Two groups of geoscientists were brought together to first view Waterways, an art exhibition, and then participate in a VM. The research concludes that the scientists were able to express tacit knowledge elicited through the experience of Waterways, enabling them to think differently about their work and form new understandings about the natural environment in relationship to themselves and society. For artists, the VM can be an effective tool when working with scientists and the public. The study argues the importance of bringing tacit knowledge to the surface, allowing greater possibility of combining scientific and artistic approaches.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".