Filling in the Blanks: Subtle Cues of Coherence, Belongingness and Meaning in Life
Bibliographic record
Abstract
Heintzelman, Trent and King (2013) suggested that feelings of meaning in life (MiL) emerge when individuals perceive subtle cues of coherence.Study 1 and 2 sought to replicate and extend Heintzelman et al.'s (2013; Study 4) finding of increased selfreported MiL after being presented with coherent, in comparison to incoherent, linguistic triads.Both attempts, however, failed to replicate.Study 3 aimed to assess whether the effect of coherence on MiL is only realized when a threat to a fundamental need (belongingness) is apparent.Using an online ball-tossing game, Cyberball, to create feelings of exclusion, participants were subsequently randomly assigned to view either coherent or incoherent word triads.They then completed measures of MiL and current mood.Coherent triads (vs.incoherent triads) had no significant effect on MiL in either the inclusion or exclusion condition.Possible explanations for these discrepant findings are discussed, and potential future directions are proposed.MTurk
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".