Videos to study Interactions in AGEing (VIntAGE)
Bibliographic record
Abstract
The Videos to study Interactions in AGEing (VIntAGE) corpus aims to investigate the complex relationship between language, cognition, and aging, focusing on verbal and non-verbal pragmatic markers in older persons with mild cognitive impairment (MCI). This multimodal and longitudinal corpus incorporates an analysis of gestural and verbal markers in discourse, aligned with neurolinguistic models. It provides a rich dataset for analyzing how aging impacts communicative competence in individuals with MCI. The VIntAGE corpus comprises approximately 18 hours of video recordings from 36 face-to-face interviews conducted by a close acquaintance of each of the nine women, all over 75 years old. Five participants were selected for in-depth analysis due to significant changes in their cognitive status. The participants underwent a series of semi-structured interviews over 15 months. The data were processed using transcription tools (for verbal discourse) and annotation tools (for gestures) and then subjected to Principal Component Analyses to manage each individual's diverse dataset and discursive modalities. The corpus includes the annotation of 6,351 verbal pragmatic markers (VPMs) and 8,044 non-verbal pragmatic markers (NVPMs). The data reveal an average decrease in MoCA scores from 23/30 to 20/30 over one year, highlighting cognitive decline's effects on verbal and non-verbal communication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.019 | 0.005 |
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".