Motivation moderates the effects of social support visibility.
Bibliographic record
Abstract
Social support can sometimes have negative consequences for recipients. One way of circumventing these negative effects is to provide support in an 'invisible' or indirect manner, such that recipients do not construe the behavior as a supportive act. However, little is known about how recipients' motivational states influence when visible (direct) support or invisible support is more beneficial. Using the framework of Regulatory Mode Theory, we predicted that recipients motivated to engage in critical evaluation (i.e., those with a predominant assessment motivation) would find invisible support more beneficial than visible support, whereas recipients motivated to initiate action (i.e., those with a predominant locomotion motivation) would find visible support more beneficial than invisible support. Findings from one 2 × 2 experiment (Study 1), two laboratory experiments (Studies 2-3), one dyadic study involving support conversations between friends (Study 4), and a meta-analysis aggregating data from all four studies supported these hypotheses. As predicted, support outcomes were better for assessment predominant recipients following invisible support, but were better for locomotion predominant recipients following visible support. Results indicate that support attempts could be made more effective by considering both support visibility and recipient motivation. (PsycINFO Database Record
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".