Independent Study 490A: What Were the Reasons for Why Adopters Choose a Black Dog Over Other Colors?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".