Mismatch Between Older Persons’ Generative Concern and Internalized Generative Capacities: Leveraging on Generative Ambivalence to Enhance Intergenerational Cohesion
Bibliographic record
Abstract
Studies have shown how generativity, the concern for establishing and guiding the next generation and safeguarding its wellbeing, functions as an intergenerational conduit, bridging the developmental stages of older individuals with those younger. Yet, applications of generativity, as a means to bridge generational gaps within rapid social change, remain underexplored in the intergenerational field. Using Singapore as a case study, and through focus group discussions with 103 older persons, this paper examines how older Singaporeans express their generative concern and internalize their generative capacities across different social settings and rapid socioeconomic transformation. Mismatch between older Singaporeans' generative concern and capacity contributes to ambivalence - mixed feelings about guiding younger generations - which emerges out of older Singaporeans' struggles with cultural change prompted by economic progress, as well as concerns about their place and value in a technologically advanced global city-state. The concept of generative ambivalence can add value to policy perspectives on intergenerational cohesion, as it considers people's attempts to forge commonalities and mutual reciprocity despite differences (e.g. gender, age, race, skills), as well as highlights intergenerational complexities beyond superficial binaries. Policies aimed at bringing generations together must be intentional in creating opportunity structures that go beyond categorical differences, where multiple generations can thrive interdependently.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".