Bibliographic record
Abstract
I was genuinely honored to be selected as lead editor for the Journal of Social and Personal Relationships (JSPR). I feel especially fortunate to be following the tenure of past editor Mario Mikulincer; besides being a personal academic hero, Mario has left JSPR in fantastic shape. Like many members of the International Association for Relationship Research (IARR), the organization has not only been of enormous benefit to me in developing my career, but I also cherish the warm and supportive atmosphere that is part of the association’s fabric. The more senior I become, the more I recognize that IARR could not function without generous service from a multitude of people, all of whom have no shortage of busyness in their lives. Although I had many motivations for pursuing this role, an important one was the opportunity to contribute to IARR in a manner that I felt suited my skill set. Perhaps the most enjoyable task I have had in the early stages of this role is selecting the outstanding group of academics who make up the journal’s associate editors: Susan Boon, Katherine Carnelley, Melissa Curran, Lara Kammrath, Erina MacGeorge, Brent Mattingly, Matthew Montoya, and Jennifer Theiss. We all share a sense of excitement at the prospect of shaping JSPR. What I would like to do here, as briefly as possible, is lay out what we see as our focus in editing the journal in order to offer some guidelines for submissions as well as touching on the responsibilities of the editors and reviewers in the endeavor of screening and publishing relationship research. All of us support the idea that one of the major strengths of both IARR and JSPR are their multidisciplinary natures. We value and solicit work from a range of perspectives as well as work that employs either qualitative or quantitative methods. Submitting authors should be aware of the range of perspectives in the JSPR readership and should be mindful of this full audience in submitting manuscripts. For example, considerations of
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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 | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.188 | 0.199 |
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".