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Record W7108079914 · doi:10.17605/osf.io/g5a3c

FROM WORDS TO FOOD - A CROSS-CULTURAL STUDY OF PARENTAL APOLOGIES AND FORGIVENESS

2025· other· W7108079914 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsForgivenessNonverbal communicationInterpersonal interactionInterpersonal communicationSelf-disclosureInterpersonal relationshipAsian culture

Abstract

fetched live from OpenAlex

In the present research, we examined if and how Chinese and Canadian parents apologize to their children after a wrongdoing, focusing on verbal vs. nonverbal apologies. In Study 1, we investigated university students who reported their parents' apology tendencies, whereas in study 2, parents reported their own apology tendencies. We expected that Canadian parents are more likely to give verbal apologies than Chinese parents, and Chinese parents are more likely to give nonverbal apologies than Canadian parents. We explored the relational and emotional consequences of giving and receiving verbal vs. non-verbal apologies, such as forgiveness and change in closeness. We also explored the potential mechanisms of the expected cultural differences including filial piety, perceived effectiveness of apologies, and relationship quality.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.054
GPT teacher head0.421
Teacher spread0.367 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueOpen Science FrameworkFrench-language works237,207