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Record W4323319219 · doi:10.3390/ani13050927

Impact of Ethical Ideologies on Students’ Attitude toward Animals—A Pakistani Perspective

2023· article· en· W4323319219 on OpenAlexaff
Asiya Khalid, Pim Martens

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRelativismIdeologyIdealismPsychologySocial psychologyPerspective (graphical)Test (biology)Scale (ratio)Political scienceEpistemologyLawGeographyPhilosophyMathematicsEcology

Abstract

fetched live from OpenAlex

Idealism and relativism are components of ethical ideologies which have been explored in relation to animal welfare and attitudes, and potential cultural differences. The present study investigated how ethical ideologies influenced attitude toward animals among undergraduate students. With the help of stratified random sampling, 450 participants were selected from both private and public sector universities in Pakistan. Research instruments consisted of a demographic sheet, the Ethics Position Questionnaire (EPQ), the Animal Attitude Scale-10-Item Version (AAS-10), and Animal Issue Scale (AIS). The study hypotheses were explored by employing various statistical analyses like Pearson Product Moment Correlation, independent sample t-test, ANOVA, and linear regression. Results revealed that there was a significant positive relationship between ethical ideologies (idealism and relativism) and attitude toward animals in students. Results further showed that students who consumed meat less frequently scored higher on relativism as compared to those who consumed meat more frequently (however, the effect size was small). It was also found that senior students held more idealistic ideologies as compared to freshman students. Finally, idealism positively predicted concern for animal welfare among students. The current study shed light on how ethical ideologies can shape and influence animal welfare. It further highlighted the potential cultural differences for the study variables by allowing for comparison with other published studies. By understanding these dynamics better, researchers will be better equipped to help students become informed citizens that may also influence future decision-making processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.871
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.236
GPT teacher head0.452
Teacher spread0.216 · 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 teacher head, 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

Citations6
Published2023
Admission routes1
Has abstractyes

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