mentioned as a role, though it is more of a descriptive term rather than a formal job title. Ecopreneurs are entrepreneurs who focus on ecofriendly or sustainable business practices.

The post-check process has limitations, as it was conducted in a separate model session. Consequently, the model did not retain memory of the specific decision made during the initial analysis. Instead, we recreated the same situation by inputting the same text and receiving the same result before asking for clarification. This method does not guarantee that the model’s reasoning process in the post-check exactly mirrors its initial reasoning, but it provides a useful approximation of the model’s decision-making logic.

Analysis of the texts from articles where green job titles were identified did not reveal any omissions of relevant green job titles. In instances where additional job titles were mentioned within these articles, they were categorized as nongreen or described as very general positions such as “managers” or “supervisors.” These titles were not pertinent to our specific focus on identifying distinctly green jobs, thus confirming the effectiveness and precision of our methodology for filtering out job titles that do not meet the criteria for this exercise.

This check also revealed that the model was cautious in suggesting that a job is considered green by the authors. For instance, Vona, Marin, and Consoli (2019) provide a table of green job titles, which they describe as putatively problematic due to the mixed tasks involved. The model did not identify these job titles as green. The exclusion of such borderline cases indicates that the prompt we used set the model to err on the side of specificity, avoiding ambiguous classifications. To include these nuanced cases in future analyses, the model’s prompt would need to be adjusted, potentially to broaden its criteria for what constitutes a green job. This adjustment could help to capture a wider spectrum of jobs, including those that are functionally ambiguous but still relevant to the discussion on green jobs.

# **5. Limitations**

We discuss key limitations of the study from two perspectives: narrow and broad.

From a narrow perspective, we note two limitations. First, we had access to the full texts of only 567 of 1,067 articles (53 percent). Our results indicate that specific job titles are more frequently mentioned within the main text rather than in abstracts. We obtained articles from reputable bibliographic databases to maintain quality control and systematically documented the retrieval process. However, industry-specific publications may contain a wider range of job titles, which O*NET actively incorporates. Our method is adaptable and can be applied to such industry texts without modification.

Second, in rare instances, our model failed to identify job titles included in noneditable figures or tables, although we did not detect such cases during our validation checks. It is technically feasible to refine the method to include such content by modifying the data ingestion phase. Our model’s final pipeline stage involved experimenting with various prompts to determine if jobs were mentioned in a green context.

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