Overly shallow?: Miscalibrated expectations create a barrier to deeper conversation.
Bibliographic record
Abstract
People may want deep and meaningful relationships with others, but may also be reluctant to engage in the deep and meaningful conversations with strangers that could create those relationships. We hypothesized that people systematically underestimate how caring and interested distant strangers are in one's own intimate revelations and that these miscalibrated expectations create a psychological barrier to deeper conversations. As predicted, conversations between strangers felt less awkward, and created more connectedness and happiness, than the participants themselves expected (Experiments 1a-5). Participants were especially prone to overestimate how awkward deep conversations would be compared with shallow conversations (Experiments 2-5). Notably, they also felt more connected to deep conversation partners than shallow conversation partners after having both types of conversations (Experiments 6a-b). Systematic differences between expectations and experiences arose because participants expected others to care less about their disclosures in conversation than others actually did (Experiments 1a, 1b, 4a, 4b, 5, and 6a). As a result, participants more accurately predicted the outcomes of their conversations when speaking with close friends, family, or partners whose care and interest is more clearly known (Experiment 5). Miscalibrated expectations about others matter because they guide decisions about which topics to discuss in conversation, such that more calibrated expectations encourage deeper conversation (Experiments 7a-7b). Misunderstanding others can encourage overly shallow interactions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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 | 0.017 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".