A <scp>10‐Year</scp> Portrait of Theorizing in Family Gerontology: Making the Mosaic Visible
Bibliographic record
Abstract
Based on a content analysis of family gerontology empirical studies in 13 journals (2009–2018), this article identifies theories currently being used and provides suggestions for future family gerontology theorizing. Family gerontologists are now using a greater range of theories than they were in the 1990s, including many middle‐range ones, and more scholars are citing multiple theories in their publications. Ways to advance family gerontology theorizing are to integrate more gerontology content into family theory textbooks, link middle‐range theories to broader general theories, and discuss how to use multiple theories effectively in research. Commonly used and emerging theories in family gerontology research can also be closely examined, and findings related to intersectionality and intergenerational ambivalence are briefly examined as examples of emerging theories used to study later‐life families.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".