MétaCan
Menu
Back to cohort
Record W4255635713 · doi:10.22215/etd/2017-12080

Perceived Excessiveness of Compensation Demands from Victimized Groups and its Consequences for Collective Guilt and Ingroup Forgiveness

2017· dissertation· en· W4255635713 on OpenAlexafffundabout
Sara Lidstone

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsCarleton University
FundersCarleton University
KeywordsForgivenessIngroups and outgroupsSocial psychologyCollective responsibilityMediationPsychologyCompensation (psychology)FeelingModerated mediationCollective identityBiology and political orientationPoliticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

This series of studies examined the consequences of perceived excessiveness in demands for compensation on ingroup forgiveness, via reductions in collective guilt.It was hypothesized that when Canadian Aboriginals were perceived as making demands that were excessive relative to the past harms they endured at the hands of Non-Aboriginal Canadians, it would result in reduced collective guilt on the part of the offender group (Non-Aboriginal Canadians), allowing them to grant greater pseudo ingroup forgiveness.It was expected that this relationship would occur as a function of political orientation; specifically, that increased excessiveness of the demands would undermine the collective guilt liberals typically experience.A series of studies were conducted to test this model.Results revealed a non-significant moderated mediation, but found evidence that excessive demands may promote collective guilt in conservatives who typically do not report feeling collective guilt.These findings have positive implications for promoting intergroup reconciliation.iii CONSEQUENCES OF EXCESSIVE DEMANDS iv CONSEQUENCES OF EXCESSIVE DEMANDS how proud he was of me.You dealt with more of my stressed-out craziness than anyone, but I am so grateful for your unfailing love and support; I couldn't have done it without you.And last but not least, a very special thank you to my greatest research ally, AlexandraZidenberg.We developed our love of research together during our undergraduate degrees at UOIT, and have continued to be an unstoppable team despite many tears and mounting insignificant results.I want to thank you for being both an invaluable academic resource, and also an invaluable friend, despite being six hours away.We have both faced a set of underwhelming circumstances over the last two years, but we did it!I look forward to continuing to work with you in the future. v CONSEQUENCES OF EXCESSIVE DEMANDS

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)0.002
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.035
GPT teacher head0.342
Teacher spread0.308 · 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
Published2017
Admission routes3
Has abstractyes

Explore more

Same topicForgiveness and Related BehaviorsFrench-language works237,207