Continuous Assessment of Interpersonal Dynamics (CAID) Joystick Materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The Continuous Assessment of Interpersonal Dynamics (CAID) approach (also referred to as the computer joystick method) allows a user to continually assess a person’s interpersonal behaviors over time as they are occurring in a videotaped social interaction. This repository entry includes the following materials associated with the CAID approach:</p> <ol> <li>The joystick monitor software</li> <li>An instruction manual for the CAID joystick monitor software, which includes: <ul> <li>Overview of the CAID joystick approach</li> <li>Instructions for using the software</li> <li>Training procedures for joystick raters</li> <li>A list of the CAID joystick-oriented publications</li> </ul> </li> <li>Associated video tutorials: <ul> <li>A demonstration of moment-to-moment interpersonal complementarity with CAID data</li> <li>An illustration of CAID coding by a trained rater</li> </ul> <li>Introductory material on how to conduct spectral analysis of CAID data: <ul> <li>A brief primer on spectral analysis</li> <li>An example dataset</li> <li>SPSS syntax for conducting spectral analysis</li> <li>Example analysis output</li> </ul> </li> </li> </ol>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it