Collaborative A/R/Tography and the Co-creation of Knowledge
Bibliographic record
Abstract
This thesis explores the notion of knowledge co-creation by way of three collaborative a/r/tographies, using arts-based research methods to analyze and disseminate findings. \n \n•\tThe first explores new histories through social fiction, connecting knowledge gained through archival research of mariner life from Newfoundland to Portugal, combining understandings between myself and Mariana Mendes Delgado. \n•\tThe second a/r/tography is produced with Marko Savard and explores artists' residencies in Quebec national parks, employing watercolour painting as contemplative praxis and sound composition as iterative listening practice. \n•\tThe third is a research project with Dra. Sara Carrasco Segovia – a post-doctoral researcher at the University of Barcelona. This inquiry was a performative reconstruction of the experience of research exchange between two emerging researchers through cartographic production. \nWe develop a holistic, co-created understanding of inquiry through a/r/tographic exploration of themes such as academic identities, visual language, pedagogies of discomfort, new histories, and transcultural experiences. Located in the post qualitative, I explore this work through the lens of posthumanism, nomadic theory, and new materiality. The following propositions ground the inquiry: research collaborators can co-create knowledge; collaborative a/r/tographies are one manner art educators can co-create knowledge. Through this, we can reconcile multiple and collective truths. Employing deliberate co-creation of knowledge, we can challenge the notion of research and knowledge as solitary and singular. I have analyzed and synthesized findings through a multidisciplinary art installation. Ceramic vessels containing electroacoustic compositions and perfumes invite viewers to explore a multisensorial pedagogy that weaves together the experience of inquiry and co-creation.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".