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Record W7042445952

Peoples perception of pet illness – is it still disenfranchised?

2023· other· en· W7042445952 on OpenAlexaboutno aff

Bibliographic record

VenueNORMA · 2023
Typeother
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyGriefPerceptionDisenfranchised griefAffect (linguistics)Scale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

Aims: the aim of this study was to investigate whether people's reactions to another person’s pet illness is viewed as appropriate by presenting two groups of participants with two vignettes, one where the pet owner is annoyed at their pet being unwell and one where they are upset at their pet being unwell. By investigating the responses of participants via an appropriateness scale we can then assess to see if pet illness and loss is still socially acceptable grief or if it is still disenfranchised grief. Method: an online questionnaire was distributed to participants (n=191). Participants were recruited through convenience sampling. The questionnaire consisted of demographic information, two vignettes, with all participants seeing one of the two vignettes, modified Witnessing of Disenfranchised Grief (WDG) questionnaire and the Toronto Empathy Questionnaire (TEQ). Results: the results of this study indicated that pet grief is not a form of disenfranchised grief and that being upset is an appropriate response to pet grief. With regard to gender, males and females do not significantly differ on empathy levels. This result indicates that those with higher levers of empathy towards their own pet have higher levels of negative affect in relation to a pet owners’ reaction to their own pet being unwell.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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