The effectiveness of parks for forest conservation is widely debated in Africa, where increasing human pressure, insufficient funding, and lack of management capacity frequently place significant demands on forests. Tropical forests house a substantial portion of the world's remaining biodiversity and are heavily affected by anthropogenic activity. We analyzed park effectiveness at the individual (224 parks) and national (23 countries) level across Africa by comparing the extent of forest loss (as a proxy for deforestation) inside parks to matched unprotected control sites. Although significant geographical variation existed among parks, the majority of African parks had significantly less forest loss within their boundaries (e.g., Mahale Park had 34 times less forest loss within its boundary) than control sites. Accessibility was a significant driver of forest loss. Relatively inaccessible areas had a higher probability (odds ratio >1, p < 0.001) of forest loss but only in ineffective parks, and relatively accessible areas had a higher probability of forest loss but only in effective parks. Smaller parks less effectively prevented forest loss inside park boundaries than larger parks (T = -2.32, p < 0.05), and older parks less effectively prevented forest loss inside park boundaries than younger parks (F = -4.11, p < 0.001). Our analyses, the first individual and national assessment of park effectiveness across Africa, demonstrated the complexity of factors (such as geographical variation, accessibility, and park size and age) influencing the ability of a park to curb forest loss within its boundaries.

Researchers and practitioners are often interested in assessing employee attitudes and work perceptions. Although such perceptions are typically measured using Likert surveys or some other closed-end numerical rating format, many organizations also have access to large amounts of qualitative employee data. For example, open-ended comments from employee surveys allow workers to provide rich and contextualized perspectives about work. Unfortunately, there are practical challenges when trying to understand employee perceptions from qualitative data. Given this, the present study investigated whether natural language processing (NLP) algorithms could be developed to automatically score employee comments according to important work attitudes and perceptions. Using a large sample of employees, algorithms were developed to translate text into scores that reflect what comments were about (theme scores) and how positively targeted constructs were described (valence scores) for 28 work constructs. The resulting algorithms and scores are labeled the Text-Based Attitude and Perception Scoring (TAPS) dictionaries, which are made publicly available and were built using a mix of count-based scoring and transformer neural networks. The psychometric properties of the TAPS scores were then investigated. Results showed that theme scores differentiated responses based on their likelihood to discuss specific constructs. Additionally, valence scores exhibited strong evidence of reliability and validity, particularly, when analyzed on text responses that were more relevant to the construct of interest. This suggests that researchers and practitioners should explicitly design text prompts to elicit construct-related information if they wish to accurately assess work attitudes and perceptions via NLP.