What do Instagram photos tell about grandfathers’ involvement in intergenerational family relationships? A manifest content analysis (MCA)
Bibliographic record
Abstract
Recent studies have increasingly underscored the pivotal role that grandparents occupy within family dynamics, leading researchers dedicated to the study of grandparenthood to investigate their contributions across diverse dimensions of familial life. However, a significant portion of this scholarship disproportionately emphasizes the experiences and influence of grandmothers, often neglecting the equally critical perspectives offered by grandfathers. Guided by the idea that photos may provide substantive insight on grandfatherhood, this exploratory study seeks to deepen the understanding of grandfathers’ roles in intergenerational families by employing manifest content analysis (MCA) of user-generated Instagram content from 2019. The analyzed data consisted of four hundred sixty-one (N = 461) images identified by the hashtag #grandfather. The focus of the analysis was on the discernible features and characteristics depicted in these photographs. Results revealed that grandfathers predominantly appeared at typical family gathering/bonding photos (34.62%). Notably, geographic representation indicates that images originate from Europe (39.23%), with a peak in photo uploads in June (21.88%). The study concludes with practical implications, limitations, and suggestions for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".