Who let the dogs in? A look at pet-friendly workplaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present an overview of the pet-friendliness trend, because despite its growth, there has been little research on the benefits and potential risks of pet-friendly workplaces. Design/methodology/approach – A general review is provided on pet ownership figures in North America and the benefits and drawbacks of pet ownership. Pet-friendly policies and practices are described, highlighting their potentially positive impact on well-being and performance. Possible concerns with pet-friendly workplaces are examined. The paper offers recommendations for organizations that are potentially interested in becoming pet-friendly. Findings – Many households in North America have pets that are considered genuine members of the family. As a result, workplaces are increasingly becoming “pet-friendly” by instituting policies that are sensitive to pet ownership. The scope of pet-friendly policies and practices ranges from simple to more complex measures. Adopting these measures can result in benefits that include enhanced attraction and recruitment, improved employee retention, enhanced employee health, increased employee productivity, and positive bottom-line results. But there are also concerns regarding health and safety, property damage, distractions, and religious preferences. Practical implications – The range of pet-friendly measures could apply to any workplace that is interested in improving their efforts toward recruitment, retention, and productivity, among others. Originality/value – This paper describes a range of efforts that workplaces can offer to enhance their employees’ work lives and is the first to provide a detailed account of the pet-friendliness trend.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it