Saying "I love you" for the first time, [2019]
Bibliographic record
Abstract
252 undergraduates (212 females; 40 males; Mage = 21.2 years old, Mdnage = 20.0; SDage = 5.2) who were currently involved in a romantic relationship (Mrelationship length = 35.8 months, Mdn = 17 months, SD = 60.0) from the University of Waterloo completed this cross-sectional, online study. Participants first completed measures of self-esteem and agreeableness. In addition, they completed a measure of the perceived risk of being the first to say “I love you.” Then, participants indicated who said “I love you” first in their relationship. Participants who indicated that they were the first to say “I love you” then completed a measure for the reasons why they did so. Similarly, participants who were not the first to say “I love you” completed a measure for the reasons why they refrained from being the first to say it . Participants then answered questions about how much they truly meant it when they told their partner “I love you” for the first time, whether they reciprocated if their partner said it first, and how far into the relationship they were when it happened as well as their confidence in this answer. Finally, participants reported demographics such as age, gender, ethnicity, relationship length, and relationship status. Additional measures included participants’ love for their partner, perceived love from their partner, relationship satisfaction, perceived partner’s self-esteem and agreeableness, attachment style, and trust in partner for exploratory purposes (e.g., to examine zero-order correlations among these variables). University of Waterloo (ORE #31930)
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".