Bibliographic record
Abstract
Using Gubrium and Holstein’s (2009) approach to narrative analysis, I examine how couples and I co-authored and co-edited shared meanings while talking about “we-ness.” I pay particular attention to my role as researcher and how I was active in inviting these conversations. I also attend to how participants and I developed joint meanings, negotiated individual and relational identities, and managed couples’ public image through processes of collaboration and control; specifically, by indicating agreement, navigating disagreements, and passing over alternative stories. Participants observed that talking about we-ness, with one another and with me, increased their sense of closeness. Thus, orienting to moments of togetherness and discussing past, present, and future experiences as a couple had implications for their understandings of “we” and “us.” I discuss the implications of my results, inviting researchers and therapists to consider how they may be active in shaping meaning- and identity-making conversations with participants and clients.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".