INFORMATIVE, DULL, INCOMPREHENSIBLE: SEEKING A COMMON VOCABULARY AROUND DATA IN LATER LIFE
Bibliographic record
Abstract
Abstract With the domestication of digital devices into the everyday, this paper offers critical reflections from two, related studies that both sought to explore how older Canadians communicate their experiences and understandings about the digital technologies and related data they encounter and use. The first study, a virtual, qualitative survey with 70 older Canadians highlighted their understandings of data, and the second, a pilot interview study with 7 older adults in their homes explored how and where data circulates in their everyday lives. Not only do our daily activities and routines rely on data, our routines are often, in turn, converted into data through our many devices (Burgess et al., 2022). Living with data, however, is an experience that differs from person to person and from group to group. Given these differences, how might we communicate about data with different groups? In both studies, we sought to explore and document participants’ own diction, images, metaphors, and concepts they articulate when thinking with and about their own data; giving them a space to speak and share “their own data stories louder than those that are being told about them by others” or by other devices (Thorp, 2021, p. 19). In this paper, we offer our observations on the challenges and difficulties (and potential ways forward) in finding a common vocabulary in research relationships between participants and researchers to speak about data and to the many ways that data circulate in and around older adults’ everyday lives.
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 | 0.041 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.030 | 0.061 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".