The Role of Second-Person Information in the Development of Social Understanding
Bibliographic record
Abstract
We consider the second-person or interactive approach to social understanding, conceived as an understanding of intentional relations. We identify five forms of second-person information - self-directedness, contingency, reciprocity, affective engagement, and shared intentions - that occur only in interactions. We assess the extent to which these forms of information are available to observers of interactions as well as to the participants of an interaction and conclude that whereas observers may gain some second-person information, interactive participants have a privileged position. We also ask whether these forms of second-person information can deliver social understanding in terms of the understanding of intentional relations that are descriptive of persons. We argue that whereas none of these forms alone is sufficient for understanding intentional relations, they all play an important role in the developmental processes that enable the construction of social understanding. Therefore, the second-person approach, understood as theorizing how second-person information available in interactions is used in the development of social understanding, is a critically important approach to a full theory of social understanding.
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.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".