Complementing the Sculpting Metaphor: Reflections on How Relationship Partners Elicit the Best or the Worst in Each Other
Bibliographic record
Abstract
A major idea in relationship science is that partners in a close relationship can “sculpt” each other in a manner that helps them align more closely with their ideal, or true, self. This sculpting metaphor is compelling, elegant, and generative, but it also possesses previously unrecognized liabilities, especially in its conceptualization of the ideal self as a sculpture yearning for release from a block of stone that is imprisoning it. Given the powerful role that metaphors play in structuring thought, overreliance on the sculpting metaphor has blinded us to certain questions even as it has sensitized us to others. To develop a comprehensive understanding of the ways in which relationship partners bring out the best or the worst in each other, we must complement the sculpting metaphor with metaphors that direct our attention to questions that it obscures, such as (a) where the ideal self comes from and (b) whether, how much, and how the ideal self changes over time.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.059 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".