Question

Passage A
For decades, the attribution of anonymous or disputed Renaissance texts relied primarily on the subjective interpretations of literary critics. Scholars engaged in traditional "close reading," meticulously evaluating thematic resonances, idiosyncratic metaphors, and stylistic flourishes to construct an argument for a particular author. However, this established approach is inherently vulnerable to confirmation bias; a critic predisposed to believe William Shakespeare authored an anonymous play will invariably locate seemingly "Shakespearean" qualities within its verse. Computational stylometry offers a vital, rigorous corrective to this interpretive subjectivity. By applying sophisticated statistical algorithms to quantify linguistic features—such as average sentence length, the structural placement of clauses, the frequency of specific function words, and overall vocabulary richness—stylometry transforms the historically opaque art of authorship attribution into a transparent, verifiable science. While stylometric analysis does not entirely eliminate the need for traditional literary interpretation, it establishes an essential, objective empirical baseline that previously did not exist in the humanities. Critics can no longer rely on vague, impressionistic assertions of "authorial voice" without engaging directly with the statistical realities of the text's unique linguistic fingerprint. Ultimately, these advanced computational methods do not replace the humanist scholar; rather, they provide the scholar with an invaluable evidentiary foundation, ensuring that academic debates over historical authorship are grounded in rigorously quantifiable data rather than mere aesthetic intuition or prevailing academic orthodoxies.

Passage B
The recent intrusion of data science into literary studies, specifically through the burgeoning field of computational stylometry, is predicated on a fundamental epistemological misunderstanding of what actually constitutes a literary text. Proponents of these quantitative methodologies treat novels, poems, and plays as mere "bags of words"—aggregates of discrete, isolated data points waiting to be mined, sorted, and statistically modeled. But a literary text is never merely a random distribution of linguistic features; it is an intentional, highly complex act of communication, deeply embedded within specific socio-political and historical contexts. By aggressively reducing literature to statistical frequencies and algorithmic patterns, stylometry strips language of its vital contextual meaning. For instance, the sudden shift in average sentence length or the anomalous adoption of specific function words in a Renaissance play might not indicate a different author or a collaborative text, as a stylometric algorithm would blindly assume. Instead, it might represent a deliberate rhetorical shift by a single author designed to parody a contemporary political figure or mimic a competing playwright. When computational methodologies isolate the text from its historical and cultural environment, they produce a sterile, profoundly ahistorical reading that misses the very essence of literary art. Furthermore, the powerful illusion of "scientific objectivity" championed by stylometric advocates masks the highly subjective human decisions inherent in programming the algorithms and selecting the parameters themselves. Genuine literary analysis necessitates a qualitative engagement with history, culture, and semantic meaning—humanistic elements that can never be adequately captured by a statistical baseline.

Which one of the following most accurately describes the primary purpose of both passages?