## 7 Conclusions and Outlook  
This paper discussed machine learning models capable of classifying bipartite skill labels in terms of their offshorability and automatability. Both indicators help assess a skill's future readiness, i.e., how likely there will be a future demand for it. Bipartite skill labels describe a skill based on two terms--the _skill topic_ (e.g., Python) and a _verb_ that provides additional context regarding the task or activity (e.g., programming). This bipartite skill specification allows distinguishing the skill of _Programming Python_ from skills such as _Teaching Python_, _Debugging Python_, or _Profiling Python_ but do not provide any context beyond this information. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **f1** & **Precision** & **Recall** & **Accuracy** \\ \hline SVM baseline & 0.52 & 0.81 & 0.54 & 0.54 \\ distilbert-base-multilingual-cased & **0.91** & **0.91** & **0.90** & **0.90** \\ ChatGPT combined with Heuristic & 0.46 & 0.58 & 0.46 & 0.46 \\ Ensemble model & 0.90 & 0.90 & **0.90** & **0.90** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance for the “automatable” indicator on the skewed rule-based dataset. The best results are indicated in bold. Our primary contributions lie in the customization and fine-tuning of deep learning methods tailored for the automatic classification of future readiness (in terms of automatability and offshorability) within the framework of bipartite skill labels. We conducted comprehensive evaluations of these methods to assess their performance and to investigate whether increasing model complexity translates into classification performance under the constraints imposed by the use of bipartite skill labels. The paper compares the performance of four skill classifiers (SVM, Transformers, GPT-based, and DistilBERT ensemble) which were trained using a gold standard dataset of 2254 annotated bipartite skill specifications obtained from the scientific literature and domain experts. A fourfold cross-evaluation assessed classification performance on the gold standard dataset and was followed by a second set of experiments that evaluated the robustness of the trained classifiers by applying them to a considerably larger dataset of 13,937 annotations created based on expert-defined annotation rules. The evaluation results indicate that domain-specific fine-tuning is essential to improve the accuracy of the classification algorithm. Once fine-tuned, even the SVM baseline outperformed the GPT classifier, which only benefited from few-shot learning through examples integrated via prompt engineering. The Transformer classifiers also leveraged the knowledge obtained during pre-training, easily outperforming models that did not benefit from pre-training. Given the scarcity of input data (the two terms obtained from the bipartite skill labels), increasing model complexity does not necessarily lead to improvements in classification performance. This result is noteworthy since the ensemble models have not outperformed the single DistilBERT model. In conclusion, assessing a skill's automatability and offshorability offers insights into the future readiness of professional skills, therefore aiding in forecasting the likelihood of future demand for specific skill sets. Through customization and fine-tuning of deep learning methods, the research evaluates the performance of skill classifiers. It provides valuable findings for improving the accuracy of classification algorithms that are limited to bipartite skill labels in predicting job market demands. Future work will integrate insights into a skill's future readiness with systems such as CareerCoach [41], which provides reskilling and upskilling suggestions. We also plan to leverage additional background knowledge in the classification process. O*NET and ESCO ontologies, for instance, encode knowledge on skills, competencies, and occupations that might be helpful in better contextualizing the classification input.