## 5 Results
### PC1: The structural lexical variety. The presence of DES indices (DESPC, DESSC, DESWC, DESWLsy, DESWLsyd, DESWLlt, and DESWLltd) suggests close correlation to text length, word count, sentence structure, and paragraph segmentation, capturing the scale and density of textual information. The inclusion of PCSYNz and PCSYNp (syntactic simplicity), PCDCz and PCDCp (deep cohesion), SYNLE (words before main verb), and PCVERz and PCVERBp (verb cohesion) indicates syntactic structuring and logical organization. Additionally, PCCNCz and PCCNCCp (word concreteness) suggests the presence of concrete discourse markers that facilitate better comprehension. CRFAO1, CRFCWO1, and CRFAOa (indices for argument overlap and content word overlap) are used to measure the lexical repetition that reflects referential cohesion. ISAPP1d and ISAGN are used to measure both the average cosine similarity of lsa between adjacent paragraphs and the average value of each sentence. LDTTRC, LDTTRa, and LDVOCD (lexical diversity) measure variation in word choice and represent the range and distribution of the vocabulary used. SMINTEr (intentional cohesion) is the ratio of intent words compared to actions or events words. Finally, RDL2  
Figure 10: **3D PCA loading plot:** principal component analysis of Coh-Metrix features across multiple dimensions, with leader lines connecting each variable to its corresponding spatial position. (readability index) suggests that PC1 reflects the reading accessibility of the texts, specifically how the structural and lexical density of a text impacts comprehension. **PC2: Semantic cohesion and logical structuring. **  
Propelled by referential cohesion (CRFNO1, CRFSO1, CRFCWO1d) as well as latent semantic analysis (LSASS1, LSAPP1), PC2 assesses the semantic coherence across sentences and paragraphs by estimating the degree to which the textual units overlap in reference or concept. The high incidence of connectives (CNCAII, CNCCaus, CNCAdd, CNCPos) suggests that a given dimension represents an element of logical structuring and discourse organization. These explicit markers of coherence (e.g., causal, additive vs. contrastive) indicate that the use of these connectives in structured units contributes to coherence with the larger discourse. Additionally, the situation model indices (SMCAUSv, SMCAUSvp, SMCAUSlsa), which relate to causative verbs and causal cohesion, assess the semantic overlap of verbs. The syntactic complexity indices (SYNMEDwrd, SYNSTRUTa, SYNSTRUTt) suggest some relationship with sentence structure, but it is also evident that paragraph level coherence is fairly important in textual connectivity. Finally, aspects of written syntactic density reference the incidence of adverbial phrases, prepositions, negations, and infinitive phrases, and the combined patterns of use constitute not only difference in density, but also fidelity of the linguistic construction. The indices of readability (RDFRE, RDFKGL) refer to the accessibility of reading the text, and higher scores indicate a more accessible text for readers. PC2 measures semantic consistency across sentences and paragraphs, capturing textual coherence, structural organization, and readability through referential cohesion, latent semantic analysis, causal relations, syntactic complexity, and readability indices. **PC3: Stability of syntactic patterns. **  
PC3 is shaped by referential cohesion (CRFCWOad), which captures the degree of content word overlap between adjacent sentences, reflecting local textual continuity. Additionally, latent semantic analysis (LSASSpd, LSAGNd) emphasizes semantic similarity in both sentence pairs and general discourse, weighing the stability of meaning connections. The presence of situation model indices (SMINTEp, SMCAUSwn) indicates the occurrence of event process words, as well as the overlap of wordnet among verbs. Moreover, syntactic pattern density measures (DRVP, DRGERUND) further delineate the structural characteristics of this component.