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Record W7079546101 · doi:10.17632/r6y32g2d2j

Girls have a more complex understanding of animal welfare than boys

2025· dataset· en· W7079546101 on OpenAlexaff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultinomial logistic regressionWelfareAnimal welfareLogistic regressionQualitative propertySelection (genetic algorithm)Qualitative researchTest (biology)

Abstract

fetched live from OpenAlex

Our study hypothesized that gender influences how children and adolescents conceptualize animal welfare. Specifically, we expected that girls would express more complex and affectively nuanced definitions of animal welfare than boys, who would provide shorter and more concrete responses. The data were collected between July and September 2023 at Buin Zoo, Chile. A total of 206 children and adolescents aged 7-18 years participated voluntarily. Using a next-across-the-line method to minimize selection bias, every second eligible child was invited. The open-ended question posed was: “What is animal welfare for you?”. Responses were audio-recorded, transcribed verbatim, and anonymized. Data collection took place before children entered the exhibits to avoid direct priming by specific animals. Two analyses were conducted: (1) Qualitative analysis (text mining and lexical network analysis) to identify gendered patterns in language use; (2) Quantitative analysis (coding responses into basic, intermediate, or advanced levels of animal welfare understanding, based on the Five Domains Model) followed by multinomial logistic regression to test associations between gender and conceptual level. Findings Qualitative Results: Girls used more diverse and affectively charged terms, forming richer lexical networks around concepts such as comfortable, stress, and calm. Their responses connected affective states with environmental and welfare conditions. Boys, in contrast, tended to produce narrower, more functional associations (e.g., place, recreation, to feed). Quantitative Results: Of the 206 responses, 130 explicitly addressed animal welfare concepts. Among these, 44.9% were intermediate, 37.8% basic, and 17.3% advanced. Logistic regression revealed that girls were four times more likely than boys to provide advanced definitions of animal welfare (OR = 4.09; 95% CI: 1.07–15.60; p = 0.03). The results indicate clear gender-based disparities in children’s conceptualizations of animal welfare. Girls not only demonstrated more advanced understanding but also expressed concepts in ways that integrated both cognitive and affective dimensions. Boys’ responses reflected simpler and more utilitarian views, often focused on concrete aspects of care. These findings suggest that: - Educational interventions should consider gender differences when designing animal welfare curricula. Programs incorporating affective and ethical dimensions may particularly help boys broaden their conceptual frameworks beyond functional care. - The use of zoo-based surveys offers ecologically valid insights, as children respond in a setting where animals are salient, though contextual influences should be acknowledged. - More than half of the sample still expressed only basic or intermediate knowledge, highlighting a gap between intuitive ideas of “care” and more comprehensive welfare frameworks such as the Five Domains Model.

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)0.001
metaresearch head score (Gemma)0.002
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: Dataset · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.127
GPT teacher head0.318
Teacher spread0.190 · 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
GenreDataset

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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