Real-life interactions and the eight sources of information framework (8-SIF): A reply to Champagne-Lavau and Moreau (2013).
Bibliographic record
Abstract
In this response to the comment by Champagne-Lavau and Moreau (2013), we acknowledge the importance of ecological mentalizing assessments that allow direct interactions between the agent and the person to whom mental states are attributed. Furthermore, we clarify that our model, the 8 sources of information framework (8-SIF; Achim, Guitton, Jackson, Boutin, & Monetta, 2013), aims to document the sources of information on which mentalizing processes can act, rather than specifying the numerous affective and cognitive processes involved in mentalizing. We argue that the sources of information that can contribute to mentalizing judgments during real or realistic interactions are included in the 8-SIF. The interaction may have an impact on the amount of information from each source that is available to the agent, but gaining additional information from a given source does not change the type of information or its classification to a specific source in the 8-SIF. The point raised by Champagne-Lavau and Moreau calls for a new comprehensive model of social cognition that focuses on the mentalizing processes, which would nicely complement our model of the sources of information on which these processes can act, the 8-SIF.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.079 | 0.109 |
| Insufficient payload (model declined to judge) | 0.004 | 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".