Revisited: Communication Media Use in the Grandparent/Grandchild Relationship
Bibliographic record
Abstract
This study extends and replicates some of Harwood’s (2000) earlier research investigating media use in interactions between grandparents and grandchildren. More specifically, this research extends Harwood’s work by adding the technologies of the cell phone, email, and instant messenger to the media he investigated (face-to-face, written documents, and telephone). Such a study allows finding out whether the availability of new technologies has any effect on the grandparent/grandchild relationship. Sixty-six dyads (N = 132) of grandchildren and grandparents participated in the study, completing a questionnaire on basic demographics, media use, and relational quality. Results show usage divides between grandchildren and grandparents, as well as within the grandparent group. The cell phone and face-to-face interaction are used most frequently in the grandparent/grandchild relationship. Technologies such as email or instant messenger are not used much even across the geographic distance they were designed to overcome. Follow up tests to a significant ANOVA did not show significant results for medium type chosen based on who initiates contact. Face-to-face remains the strongest predictor of quality inter-generational relationships, followed by use of the cell phone, the landline phone, and email (in that order). Findings are discussed in light of both media richness theory and the social influence model.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".