Reflections on multimodality: Making the most of Kairotic moments
Bibliographic record
Abstract
It is generally accepted in extant literature that friends drift away after a person receives a diagnosis of dementia. In turn, we set out to explore friendships that continued to flourish following a diagnosis by interviewing people living with dementia, their friends, and family members. Along the way, we shaped and adopted a multimodal approach, incorporating artistically rendered, fictionalised vignettes based on our participants’ stories, thus incorporating visual and auditory components that encourage people living with dementia and their friends to reflect on how best to continue to nurture their relationships. In this article, we describe our process of adopting multimodality through an intertwined set of five kairotic moments, whereby we pushed ourselves out of our comfort zones to move beyond the format of the conventional peer-reviewed journal article, recognising the need to write differently to reach a broader audience. In another moment, we moved past an academic emphasis on writing to adopt multimodality. Subsequently, we connected with artists and knowledge mobilisation specialists to bring our collective vision to life. Finally, we aimed to make our study findings more accessible by sharing them through our website and engaging with various types of media. We conclude by offering a methodology for multimodality that includes relationality, axiology, passivity and action and temporality in embracing opportunities to write differently.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".