Independent Study 490A: What Were the Reasons for Why Adopters Choose a Black Dog Over Other Colors?
Notice bibliographique
Résumé
Many animal shelters across the country have noticed that black dogs are not adopted as quickly as lighter colored dogs. This trend goes even further as a dog that is all black is not adopted as quickly as a black dog with one white toe or other small white flashing. Therefore, the objective of this study was to determine the reasons why an adopter chose a black dog over other colors. This study was performed at the Animal Rescue League (ARL) of Iowa, located in Des Moines IA. A total of 13 canine records were used in this study. The dogs that qualified for this data set had been 100% black (solid). The three questions asked of each adopter were as follows; (1) What attracted you to a black dog? (2) Have you had a black dog before? and (3) If no to question 2, are you familiar with a black dog from friends’ family or the neighborhood? In addition, the type, age, sex and how long that dog was at the ARL was also collected. A variety of dog breeds were involved in the study with the most popular all black dog breed being classified as a Labrador mix (8) respectively. What attracted you to a black dog? The most commonly cited reason for why a black dog was being adopted was based on appearance (n = 4). Next, breed (n = 2), color did not matter (n = 2) and previous ownership (n = 2) were cited. “Other” (n = 3) included an emotional connotation “he is big and smart” “I felt lucky” and “I love black dogs.” Have you had a black dog before? Of the 13 responses, 8 said that they had owned a black dog in the past (Figure 1). If no to question 2, are you familiar with a black dog from friends’ family or the neighborhood? Of the 13 responses, 12 were familar with a black dog before. Therefore in conclusion, the most common reason cited for adopoting a black dog was based on dog apperance. Even though 8 out of 13 adopters had owned a black dog before, previous ownership was only cited twice. Furthemore, the majority of adopters were familiar with a black dog and it could be hypothezied that this interaction was favorable.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».