## 4 Results
### Qualitative and Quantitative Error Analysis  
To comprehensively assess model performance, we conducted qualitative and quantitative analyses of model errors using confusion matrices. This evaluation provided insights into frequent sources of false positives (FP) and false negatives (FN). Confusion matrices (see Table 10) showed BETO models generally had better recall and balanced precision, particularly evident in the single-turn variants for both racism and sexism. For instance, BETO single-turn racism achieved a precision of 83.6% and a recall of 79.3%, highlighting robustness in capturing racist dialogues. Conversely, BOW models exhibited higher precision but notably lower recall, as demonstrated in BOW single-turn sexism with a precision of 69.2% but a low recall of 19.6%. Next, we illustrate the typical errors with selected qualitative examples:  
* False negative (racism, BETO CTX): \"Que ahora tengamos 1123 hijos por pareja no quiere decir que en 2090 vayamos a tener 0. Seguimos siendo superiores intelectual \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Variant** & **TP** & **FP** & **FN** \\ \hline \multicolumn{5}{c}{_Racism Detection_} \\ \hline LogReg baseline & ST & 31 & 5 & 27 \\ LogReg baseline & CTX & 25 & 2 & 30 \\ HF BETO (SpaCy) & ST & 46 & 9 & 12 \\ HF BETO (SpaCy) & CTX & 41 & 14 & 14 \\ TextCatBOW (SpaCy) & ST & 29 & 3 & 29 \\ TextCatBOW (SpaCy) & CTX & 33 & 4 & 22 \\ \hline \hline \multicolumn{5}{c}{_Sexism Detection_} \\ \hline LogReg baseline & ST & 11 & 10 & 35 \\ LogReg baseline & CTX & 21 & 8 & 28 \\ HF BETO (SpaCy) & ST & 35 & 24 & 11 \\ HF BETO (SpaCy) & CTX & 35 & 20 & 14 \\ TextCatBOW (SpaCy) & ST & 9 & 4 & 37 \\ TextCatBOW (SpaCy) & CTX & 25 & 14 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confusion matrices for selected BETO, BOW, and logistic regression models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Setting** & **Model Pair** & **F1 Difference** & \(p\)**-Value** \\ \hline \multicolumn{5}{c}{Racism Detection} \\ \hline ST & BETO vs. BOW & 0.814 vs. 0.644 & 0.0522 \\ ST & BETO vs. LogReg & 0.814 vs. 0.660 & 0.0614 \\ ST & BOW vs. LogReg & 0.644 vs. 0.660 & 1.0000 \\ CTX & BETO vs. BOW & 0.745 vs. 0.717 & 0.8506 \\ CTX & BETO vs. LogReg & 0.745 vs. 0.610 & 0.5966 \\ CTX & BOW vs. LogReg & 0.717 vs. 0.610 & 0.2101 \\ \hline \multicolumn{5}{c}{Sexism Detection} \\ \hline ST & BETO vs. BOW & 0.667 vs. 0.305 & 0.4799 \\ ST & BETO vs. LogReg & 0.667 vs. 0.328 & 0.2203 \\ ST & BOW vs. LogReg & 0.305 vs. 0.328 & 0.4807 \\ CTX & BETO vs. BOW & 0.673 vs. 0.568 & 0.5966 \\ CTX & BETO vs. LogReg & 0.673 vs. 0.538 & 0.8506 \\ CTX & BOW vs. LogReg & 0.568 vs. 0.538 & 0.8318 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: [PERSON]’s exact test \(p\)-values (two-tailed) comparing paired model predictions for single-turn (ST) and contextual (CTX) settings on sexism and racism dev sets. Bold marks \(p<0.05\) (statistically significant). mente (Que es lo que hace predominant una raza sobre otra)...\". English translation: \"The fact that we have 1123 kids per couple does not mean that in 2090 we will have 0. We continue to be superior intellectually (Which is what makes a race predominate over another)\". This dialogue explicitly mentions intellectual racial superiority, yet BETO CTX failed to detect racism. * False positive (sexism, BOW CTX): \"...En la empresa privada, si hay que demostrar mas, porque si no, te crujen, que siempre hay alguien jugandose la pasta. Sobre todo en puestos de cierta responsabilidad. Anda que no hay becarios haciendo el trabajo a \"personas hechas a si mismas\" que son hijos del jere\". English translation: \"...In the private sector, you do have to prove more, because if you don't, they'll crack you, because there's always someone who's putting their money on the line. Especially in positions of some responsibility. There are no interns doing the work of'self-made people' who are the boss's kids\". While the content might be controversial, it does not inherently reflect sexism.