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Record W6923798299 · doi:10.15139/s3/nozyud

Relocation and Romantic Relationships - Longitudinal, 2019-2022

2022· dataset· en· W6923798299 on OpenAlexaffabout

Bibliographic record

VenueUNC Dataverse · 2022
Typedataset
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelocationAttritionRomanceService (business)Sample (material)

Abstract

fetched live from OpenAlex

We obtained a sample of 455 participants (i.e., 227 couples and 1 individual) who relocated to a different city, state/province, or country, with one partner who initiated the couple’s move (i.e., relocaters, who typically moved for career opportunities), and the other partner who accommodated their partner’s initiation to move (i.e., trailers). Couples moved an average of 2,702 kilometers (range: 18km to 15,535km), with some moving to a different city (23.6%), most moving to a different province/state (46%), and others moving internationally (29.8%). Participants filled out a baseline survey ~2 months before couples moved, 5 shorter bi-weekly surveys in the wake of the move, and follow-up surveys at 3, 6, 9, and 12 months post-move. Attrition was relatively low but increased over time (Ns: 1st bi-weekly = 423, 2nd bi-weekly = 406, 3rd bi-weekly = 400, 4th bi-weekly = 395, 5th bi-weekly = 384, 3mo follow-up = 365, 6mo follow-up = 347, 9mo follow-up = 326, 12mo follow-up = 288). Some attrition is due to couples that broke up over the course of the study (N = 38, 8.3%). Participants were recruited via a wide variety of ways, such as through moving service companies, relocation offices of large companies, universities, and hospitals, and various online networking sites (e.g., kijiji, Craigslist, Reddit, Facebook groups). Couples were eligible when both partners spoke English, were over the age of 18, were in a romantic relationship, currently lived together, and importantly, when they were going to relocate with their partner in at least two months, which was primarily for one of the partners (e.g., to support their career opportunities). Interested couples were enrolled after they had a phone call with a research team member to confirm their eligibility and explain the study procedure. Each participant received $10 CAD for the baseline survey, $7 CAD for each bi-weekly survey (5 bi-weekly surveys X $7 CAD = $35 CAD), and $15 CAD for each follow-up survey (4 follow-up surveys X $15 CAD = $60 CAD). Participants also received a bonus of $15 CAD if they completed all of the study surveys or all but one of the study surveys. In total, participants could receive up to $120 CAD ($240 CAD per couple), or the equivalent in another currency. Participants ranged in age from 18 to 54 (M = 29.8, SD = 5.8), 51% identified as women, 46.6% as men, 2% as non-binary, 0.2% as transgender, and 0.2% as agender. The majority identified as heterosexual (80%), with others identifying as bisexual (7.9%), lesbian (3.3%), queer (2.2%), asexual (2.2%), gay (1.8%), pansexual (1.8%), or “other” (e.g., androsexual; 0.9%). Most participants identified as White (North American/European, 62.9%) and others identified as East Asian (8.8%), South Asian (8.1%), Black (7.5%), Latin American (4.2%), bi- or multi-ethnic (e.g., White/Black, 4.6%), Native American/First Nations (0.7%), or “other” (e.g., Middle Eastern, South-East Asian, 3.3%). All participants were living together with their partner and were in their current relationship for 6.26 (SD = 4.98) years on average. Most participants were married (47.5%) or engaged (9.2%), while others indicated they were dating (27.5%), common-law (14.3%), or “other” (e.g., domestic partnership, 1.5%). About a quarter of the participants had children (23.1%), with most of these having one (13.4%) or two (8.4%) children. This project was approved by the University of Toronto research ethics board on December 14, 2018 (#00036971).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1490.006

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.

Opus teacher head0.046
GPT teacher head0.313
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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