Autobiographic episodes as languaging
Bibliographic record
Abstract
The purpose of our program of research is to explore the role of languaging on the part of older adults residing in long-term care facilities. We suggest that languaging-based activities can enhance the quality of life of such older adults including aspects of their cognition and affect. Languaging is the use of language to mediate cognitive and affective processes (Swain, 2006, 2010). In this case study, Mary (a resident) engages in the effortful re-construction of autobiographic episodes. Through microgenetic analysis we document changes in Mary’s emotional response to recreating aspects of her life history, and a change in her cognition involving a shift from other- to self-regulation in her ability to remember past events. We argue that Mary’s narration of past events (a type of languaging) is related to her positive affective and cognitive changes; this is consistent with Vygotsky’s view that cognition and affect are inextricably intertwined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".