Social Expertise: A Development of ‘Intersubjective Maximal Grip’ (IMG)
Bibliographic record
Abstract
The aim of this thesis is to supplement the interactionist alternatives to folk psychology. Briefly stated, whereas the proponents of folk psychology claim that interpersonal understanding centrally involves the attribution of propositional attitudes, such as beliefs and desires, in order to predict and explain behaviour, the interactionists argue that social understanding is a skill that encompasses a number of different aspects, which includes, but is not limited to, the attribution of propositional attitudes. Since the interactionists’ arguments are relatively new in the social understanding debate, many aspects of the arguments are not fully developed and explored. For instance, Hanne De Jaegher claims that the concept of “social skill” needs further development (2009, p. 427). This thesis aims to do just that by integrating two separate debates: social understanding and expertise. Drawing on Hubert Dreyfus’ non-representational/non-propositional account of expertise, I describe, in detail, a form of social interaction (or ‘social expertise’) that does not centrally involve the attribution of propositional attitudes. Central to Dreyfus’ discussion of skillful coping is Maurice Merleau-Ponty’s concept of ‘maximal grip’ (MG), which refers to the way we seek to obtain a better perspective in a situation via our body. Building on this discussion, I develop a new concept called ‘intersubjective maximal grip’ (IMG), which describes a way we interact with others by anticipating their behaviour by recognising and responding to our respective optimal positions. In explaining this phenomenon further, I expand on Dreyfus’ discussion of MG to develop two skills related to understanding others: ‘joint optimal position recognition’ (JOPR) and ‘optimal position recognition’ (OPR). I then apply IMG to the debate about social understanding to demonstrate how IMG supplements the interactionists’ arguments against folk psychology.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".