Purrfecting Kitten Welfare in Foster Care: Impacts of Early Life Experiences on Fear Behaviour in Kittens
Bibliographic record
Abstract
This thesis aimed to improve understanding around the identification and mitigation of fear in kittens in foster care within their socialization period. First, I examined which behaviours kittens show when avoiding novel and unpredictable stimuli and found that kittens (n=46) displayed significantly longer durations of piloerection, freezing, and tail tucking, shorter durations of putting their tail upright and eating, and greater rates of putting their ears back during fear trials compared to blank trials. Responses were also affected by kitten coat colour, sex, and being mother-reared. Next, I investigated human abilities to rate fear levels of kittens and the efficacy of specialized training in identifying kitten behaviour and found that participants who received the specialized behaviour training (n=388) had significantly greater odds of being correct for all fear categories compared to participants who received general information on kitten care (n=373). I then investigated risk factors for kittens (n=235) displaying fear behaviours during interactions with unfamiliar people and objects just prior to adoption as reported by the foster parents (n=72). I found that kittens who were reported fearful at intake into foster care had greater odds of displaying fear behaviours towards unfamiliar people and objects than kittens who were not reported fearful at intake. Further, kittens who received high amounts of exposure to non-social stimuli and who had a foster parent with a high score for emotional stability had greater odds of displaying fear towards people, highlighting the importance of accurately identifying when kittens are fearful and adapting socialization practices appropriately. Finally, I used a mixed-method survey to further understand the perspectives of foster parents (n=487) on current practices around kitten care and socializing fearful kittens, necessary resources, such as shelter-supplied resources, personal knowledge, and external support, and challenges foster parents face, such as time constraints and lack of shelter support, that are relevant to optimal kitten behavioural development and welfare. This thesis adds to our understanding of fear in young kittens, and how to optimize kitten welfare in foster care.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".