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Record W4410017028 · doi:10.1098/rsos.250508

Revisiting the effects of helper intentions on gratitude and indebtedness: Replication and extensions Registered Report of Tsang (2006)

2025· article· en· W4410017028 on OpenAlexaff
Chi Wai Chan, Fung Yee Lau, WY Ip, Chak Fong Shannon Lui, Katy Y. Y. Tam, Gilad Feldman

Bibliographic record

VenueRoyal Society Open Science · 2025
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGratitudeComputer scienceAlgorithmArtificial intelligencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Gratitude and indebtedness are common emotions in response to a favour, yet research suggests that they are experienced differently depending on the situation. Tsang (Tsang JA. 2006 The effects of helper intention on gratitude and indebtedness. Motiv. Emot. 30 , 198–204. ( doi:10.1007/s11031-006-9031-z )), found that gratitude for a favour depended on perceived helper intention, whereas indebtedness did not. Perceived benevolent helper intentions were associated with higher gratitude from beneficiaries compared to selfish ones, yet had no associations with indebtedness. In a registered report with a United States Prolific student sample ( n = 759), we conducted a replication and extensions of studies 2 and 3 from Tsang, 2006. In the original studies, Tsang found support for the impact of the helper’s intention on gratitude (study 2: η p 2 = 0.20 [0.08, 0.32]; study 3: η p 2 = 0.14 [0.03, 0.26]), but not for indebtedness (study 2: η p 2 = 0.01 [0.00, 0.08]; study 3: η p 2 = 0.00 [0.00, 0.03]). In our replications, we found support for the impact of helper’s intention on gratitude (study 2: η p 2 = 0.33 [0.28, 0.37]; study 3: η p 2 = 0.16 [0.12, 0.20]), and—as expected—no support for an effect on indebtedness (study 2: η p 2 = 0.00 [0.00, 0.01]; study 3: η p 2 = 0.01 [0.00, 0.01]). We concluded a successful replication, that helping intent was more strongly associated with gratitude than with indebtedness. Extending the replication, we found evidence for the impact of helper intention on perceived expectations for reciprocity ( d = 1.51 [1.31, 1.71]), and reciprocity inclination ( d = 0.66 [0.48, 0.84]), and for opposite associations of perceived reciprocity expectations with gratitude ( r = −0.28 [−0.35, −0.22]) and indebtedness ( r = 0.17 [0.10, 0.24]). Materials, data and code are available on: https://osf.io/ghfy4/ . This registered report has been officially endorsed by the Peer Community in Registered Reports: https://doi.org/10.24072/pci.rr.100788 .

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 imitation

Not 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.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.094
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0030.004
Open science0.0040.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.063
GPT teacher head0.405
Teacher spread0.342 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReproducibility
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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