Cross-disciplinary research methods to study technology use, family, and life course dynamics: lessons from an action research project on social isolation and loneliness in later life
Bibliographic record
Abstract
This chapter reports on the design and implementation of cross-disciplinary research methods for investigating technology adoption in later life as well as family and life course dynamics. Drawing on a mixed methods, action research project on technology and social connectedness, facilitated by a team of sociologists and human–computer interaction (HCI) researchers, it examines the use of a digital communication technology to study social isolation and loneliness in later life. The chapter first provides an overview of the deployment and feasibility design of the study, the deployment stages and procedures, data analysis and participants before discussing the lessons learned. It concludes with an assessment of the challenges and opportunities of cross-disciplinary and mixed-method research to study technologies, families, and the life course. One of the ways that cross-disciplinary mixed methods approaches can enhance family and life course studies is by capturing the immediacy of life transitions.
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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 | 0.046 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".