Bibliographic record
Abstract
In this paper I discuss the adoption of artistic research creation methodologies, the creation and exhibition of artistic works closely aligned with scholarly research, as a way to increase public engagement with academic research. I begin by discussing the need for scholars to consider the ‘public first’ when developing research communication plans, and draw upon the emergence of ‘mobile first’ interface design as a metaphor. With mobile first development, also known as progressive enhancement, ‘You start by establishing a basic level of user experience that all browsers will be able to provide when rendering your web site,’ but you also build in more advanced functionality that will automatically be available to devices, such as desktop computers (W3C 2015). I argue that we need to prioritize public first research outputs if we are truly serious about engaging the public in our research. I then move into a discussion of various research creation methodologies and explain how they are similar to, and differ from, critical making, another emergent humanities research practice that is based upon the making of physical objects. Finally I provide examples of successful research creation activities, including some related to my current SSHRC-funded project, The Post-Digital Book Arts.
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 | 0.109 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.025 | 0.062 |
| Scholarly communication | 0.050 | 0.033 |
| Open science | 0.005 | 0.054 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".