Development and validation of the gift reciprocation anxiety scale (GRAS) for youths and adults in intimate relationships
Bibliographic record
Abstract
= 763). Next, the adequacy of the data for factor analysis was checked and exploratory factor analysis (EFA) was done, extracting a single factor structure which was confirmed through the same factor retention using parallel analysis (PA). Model fit indices of confirmatory factor analysis (CFA) validated the unifactorial solution of GRAS. In addition, the item response theory (IRT) analyses confirmed that the items of the GRAS had high discriminative power, satisfactory threshold parameters, and covered a wide range of the latent trait. Mean inter-item correlations, corrected item-total correlations, and internal consistency reliabilities of the newly developed GRAS fall within the suggested limits. Multi-group confirmatory factor analysis (MGCFA) revealed that the GRAS can invariably be applied across gender, age, and marital status. A moderately positive association of GRAS with reciprocity anxiety, depression, and anxiety indicated the convergent validity of the scale. Altogether, GRAS has been found to be a psychometrically sound tool to objectively measure gift reciprocation anxiety in close relationships, implicating gift reciprocation less as an obligation and more as signs of trust, commitment, security, and care for ensuring better intimate relationships.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".