WHY GRANDPARENTS TELL STORIES: INTRODUCING THE INTERGENERATIONAL STORYTELLING FUNCTIONS QUESTIONNAIRE
Bibliographic record
Abstract
Intergenerational storytelling between grandparents and their grandchildren is a developmentally meaningful and mutually enjoyable shared activity. Until now, we know little about grandparents’ motivations for telling stories to younger generations. Do grandparents tell stories to entertain, to teach life lessons, or to simply pass the time? To cast light on this issue, we systematically investigated grandparents’ reasons, and grandchildren’s perceptions of their grandparents’ reasons, for intergenerational storytelling. We asked samples of grandchildren and grandparents in Canada and Austria to complete the newly developed 61-item Intergenerational Storytelling Functions Questionnaire (ISFQ) that measures 12 potential reasons for storytelling. A preliminary factor analysis of the ISFQ, presented here for the first time, suggests that the 12 hypothesized reasons reduce to five factors. Listed in the order of their reported frequency, the factors have been labelled: positive reminiscence function, intimacy/entertainment function, wisdom/teaching function, familial/cultural heritage function, and passing-time function. To supplement these analyses, before completing the ISFQ, grandchildren and grandparents were asked to provide up to four open-ended reasons for intergenerational storytelling. A qualitative analysis of their responses indicates that we failed to capture one additional functional with the ISFQ. We have called this the negative reminiscence or ‘processing the past’ function, in which grandparents tell stories of negative life experiences (a common example being war stories) many years later in order to make sense of the experience in the present. We hope these preliminary results spark interest in studying the underlying motivations for intergenerational storytelling, for which we provide a new measurement tool.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".