Introduction

Manuel González Zeledón (“Magón”) presents in El Clis de Sol a deceptively simple rural anecdote that conceals a deeper social critique in Costa Rica. The story, told through the voice of the narrator who recounts what his friend ñor Cornelio Cacheda told him, uses irony, folk beliefs, and character contrast to explore themes of superstition, innocence, and social stratification. Using The Arrangement of Events in a Story framework created by Prof. Acuña-Solano (n.d.) for the sequencing of event in stories, the narrative structure will be analyzed focusing on Cornelio as a central figure whose personality and worldview embody the story’s social commentary and criticism.

Sequence of Events and Use of Quotation

The narrative of Magón’s short story begins with an explicit framing:

“No es cuento, es una historia que sale de mi pluma como ha ido brotando de los labios de ñor Cornelio Cacheda” (González Zeledón, Magón, 1896/ n.d., p. 1).

      This opening establishes that what follows is not pure fiction but a retelling of Cornelio’s own testimony and life experience, lending a sense of authenticity while immediately introducing the character. The subsequent events unfold linearly: Cornelio comes to the narrator with his twin daughters. Cornelio explains (in his own colorful voice) the strange circumstances of their birth, links them to a past solar eclipse (“clis de sol”), and offers his reasoning involving his wife’s behavior, divine power, and the explanation given to him by an Italian maestro.

The chronological structure is deliberate: the explanation builds gradually, as Cornelio unpacks his story with humor, pride, and conviction. There is no flashback or complex rearrangement, which keeps the focus on Cornelio’s account and highlights how his worldview emerges naturally from his day-to-day life.

Plot Actions, Conflict, and Irony

The core of the action found in the short story’s plot revolves around Cornelio’s recounting of his twin daughters’ birth, his interpretation of the eclipse, and what this solar phenomenon caused in his wife, Lina. One of the most telling moments found in his recounting of the story is when Cornelio says:

“Usté sabe que hora en marzo hizo tre años que hubo un clis de sol … como unos veinte días antes Lina, mi mujer, salió habelitada de esas chiquillas … siempre ispiando pal cielo” (González Zeledón, Magón, 1896/ n.d., p. 2).

Here, Cornelio connects his wife’s strange restlessness, her constant watching of the sky, and the solar eclipse. In his way of making sense of what has happened to Lina, he believes, and he sincerely argues, that the eclipse had a supernatural influence on the birth of the twins, which, to modern scientific eyes, is obviously misguided, and to 19^th century eyes, is suspicious and not credible. But Magón does not merely mock Cornelio while listening to his tale; instead, he stages this logic with affectionate irony, something Cornelio, in his “simplified” mind is not able to comprehend.

What the reader witnesses through his reading of the Magón’s story is that there is a Society vs. Individual conflict (Cornelio’s beliefs vs. scientific / educated perspectives) but also an Individual vs. Self dimension: Cornelio is proud of his explanation, yet he seems aware that others might think him ignorant. He laughs when he says:

“¿Pos no había de rirme, don Magón, cuando veo que un probe inorante como yo … sabe más que un hombre como usté que todos dicen que es tan sabido …?” (González Zeledón, Magón, 1896/ n.d., p. 1).

This line in the story reveals his self-deprecating humor and also his challenge to social hierarchies: the “ignorant” campesino claims a kind of epistemic authority over the learned narrator. Cornelio Cacheda has “the explanation” as to why his twin daughters are very different from what he looks.

Character Analysis: Ñor Cornelio Cacheda

Personality and Social Position

Ñor Cornelio Cacheda is portrayed by Magón as a rustic, uneducated campesino, but one with a strong sense of dignity, family pride, and religious faith. The narrator describes his daughters:

“ambas rubias como una espiga, blancas y rosadas como durazno maduro … lindas como si fueran ‘imágenes’” (González Zeledón, Magón, 1896/ n.d., p. 1).

At the same time, Cornelio is described in stark contrast:

“feo si los hay, moreno subido y tosco hasta lo sucio de las uñas y lo rajado de los talones.” (González Zeledón, Magón, 1896/ n.d., p. 1).

This contrast amplifies the absurdity of Cornelio being the father of these two blonde, white-skinned girls (and humor) and of his pride for having fathered these cute kids around the solar eclipse despite his rough appearance, he is the “tata” (dad) of two extraordinarily beautiful daughters.

Beliefs and Worldview

Cornelio’s belief in the eclipse’s supernatural power demonstrates his deeply held religious and folk worldview. He attributes the girls’ unique features not to genetics (as now understood scientifically) but to a miraculous influence of God:

“pal gran poder de mi Dios no hay nada imposible” (González Zeledón, Magón, 1896/ n.d., p. 1).

He also invokes a foreign “mestro italiano,” a learned man in the idea of Cornelio, who supposedly explained the phenomenon to him:

“él jue el que m'explicó la cosa del clis de sol” (González Zeledón, Magón, 1896/ n.d., p. 3).

This combination of local faith and a supposedly authoritative outsider gives Cornelio’s reasoning both credibility (in his own eyes) and a touch of comic irony.

Function in the Story

Cornelio is more than just a narrator’s source: he is the story. His personality and way of understanding his surrounding world drive the narrative; his beliefs and way of speaking define the tone in the story. He embodies the tension Magón wants to highlight: rural simplicity vs. intellectual pretension, tradition vs. modern knowledge. Through Cornelio, Magón critiques social hierarchies (educational, cultural) without condemning the person; Cornelio is sympathetic, proud, humorous, and convinced in his own truth or what he understands as the truth coming from facts he is not able to analyze.

Irony and Social Commentary.

Magón’s portrayal of Cornelio is layered: he uses irony to show that Cornelio’s logic is flawed, but he also respects Cornelio’s sincerity and his status as a storyteller. Critical scholarship supports this reading: Garita Hernández and Quesada Guzmán (1987) argue that in El Clis de Sol, Magón uses irony, parody, and satire to show how social “truths” (superstition, tradition) can be accepted uncritically. Furthermore, Corrales Arias (2009) asserts that Magón’s humor “installs a lie as social truth” to expose cultural tensions. And for the Costa Rican Spanish reader, who understands the irony behind ñor Cornelio’s last name, Cacheda, derived from the word “cacho” (horn in English) and linked to the Spanish expression “poner los cachos” (to cheat on someone in English), it is quite evident that Lina, Cornelio’s wife, cheated on him with the Italian maestro.

Conclusion

By integrating quotations from the SINABI edition of El Clis de Sol and applying your structural framework, we see how Magón constructs a simple but deeply ironic narrative. The chronological, straightforward sequence allows Cornelio’s worldview to be presented fully. Cornelio himself emerges as a central character: his pride, humility, faith, and “ignorance” all contribute to the story’s humor, but also to its critique of social and cultural divides. Through him, Magón invites readers to reflect on how “knowledge” is socially constructed and how humility and humanity persist even in flawed reasoning.

📚 References

Acuña-Solano, J. (n.d.). The arrangement of events in a story. Unpublished instructional document.

Corrales Arias, A. (2009). Magón: La insoportable sospecha de la tradición. Comunicación, 18(2), 40–44. revistas.tec.ac.cr

Garita Hernández, F. M., & Quesada Guzmán, M. (1987). “Los indicios de la ironía en el cuento ‘El clis de sol’.” LETRAS, 15–17, 337–350. Revistas UNA

González Zeledón, M. [Magón]. (1896). El clis de sol. In El clis de sol (edición PDF). Sistema Nacional de Bibliotecas, Costa Rica. Sinabi+1 

Handout A: Reader Interaction Guide for blog post

Reader Interaction Guide for Blog Post by Jonathan Acuña

Introduction

In recent years, educational institutions have rushed to deploy algorithmic “solutions” to concerns about generative AI tools (e.g., ChatGPT, Claude AI, Gemini, Deep Seek, etc.) being used to produce student work. One prominent example is Turnitin’s AI writing detection module, which claims to identify text likely produced by an AI rather than a human (Turnitin, n.d.-c). While the goal of preserving academic integrity is legitimate, the implementation of such detection as “quasi-judicial evidence” is deeply problematic. Cases have already emerged of students being falsely flagged as AI-generated texts. In my very own experience, Turnitin has flagged essays I composed in 2010, long before the popularization of AI writing systems, as AI-generated, but how come? Such errors cast doubt not only on Turnitin’s technical claims, but on the ethics of using such a system to police human scholarship and penmanship.

The intention of this essay is to examine the major deficiencies of Turnitin’s detection approach: (1) its susceptibility to false positives, (2) opacity and lack of verifiability, (3) algorithmic bias, and (4) misuse in academic procedures. Finally, it outlines recommendations for more humane, transparent, and just approaches to dealing with AI in education.

False Positives: When Human Writings Are Falsely Flagged

One of the gravest defects in Turnitin’s AI detection is its tendency to flag purely human-authored content as AI-generated. Turnitin refers to such misclassifications as false positives, meaning the system labels human text as AI. The company itself acknowledges a small, but nonzero, false positive rate (less than 1 %) under ideal conditions (Turnitin, n.d.-a). So, is my writing style curated over more than 30 years of work at the university level misclassified as false positive?

Independent analyses suggest the false positive rate is much higher and context-dependent. Reports have indicated “higher incidence of false positives” when less than 20% of a document is flagged as AI-generated (K-12 Dive, 2023). Empirical testing of AI detectors, including Turnitin’s, has shown that error rates can exceed 10% in uncontrolled settings (Weber-Wulff et al., 2023). In practice, as in my own situation, decades-old writing, long predating modern AI systems, can trigger the detector. That fact alone undermines any claim that the system reliably differentiates AI versus human provenance. If a tool misfires on known human work, its verdicts on ambiguous texts carry no weight.

Opacity and Lack of Verifiability

Beyond error rates, Turnitin’s AI detection suffers from conceptual opacity. Unlike its plagiarism component, which highlights matched passages and links to original sources, the AI detection report offers no “traceback” to a source of suspicion. The system does not allow instructors or students to verify which phrases or sentences prompted the AI label (Salem, Fiore, Kelly, & Brock, n.d.). Because there is no “original text” to which flagged content can be traced, users are left entirely in the dark about why the system made its call.

That opacity severely weakens any claim that the system used by Turnitin is fair or evidence-based in academic adjudication. A student cannot counter or refute the detection logic, especially when the system itself offers no human-readable rationales. In an adjudicative environment, suspicion without scrutiny is a violation of procedural fairness especially if learners cannot defend their positions regarding Turnitin’s verdict.

Algorithmic Bias and Disparate Impact

AI detection systems, including Turnitin’s, may also exacerbate existing inequalities. Several studies suggest that non-native English writers or writers with simpler or more repetitive style are disproportionately flagged (The Markup, 2023). Controlled tests across multiple detectors found that non-native English writing was misclassified as AI-generated in over 60% of cases, whereas native English texts rarely triggered such misclassification (Stanford Human-Centered Artificial Intelligence [HAI], 2023). In my particular case, I’m not a native speaker but someone with C2, better than many educated native speakers, so my texts are flagged as if I have generated them with AI.

A cross-detector study by Liang, Yuksekgonul, Mao, Wu, & Zou (2023) confirmed systematic bias: non-native English writers’ texts were more likely misclassified as AI than native writers’ texts, even when both were human-authored. This finding suggests that algorithmic decisions are entangled with linguistic privilege, penalizing students already disadvantaged by language or cultural background.

Although Turnitin has responded that its detector shows “no statistically significant bias” for English-language learners in certain internal tests (Turnitin, n.d.-b), the lack of transparency regarding its data sets and methods makes such claims unverifiable.

Misuse in Academic Procedures: The Perils of Reliance

When an imperfect, opaque system is elevated to a quasi-authority in academic integrity processes, its defects become not merely technical flaws but instruments of injustice. Institutions sometimes use Turnitin’s AI flag as “prima facie” evidence of misconduct, shifting the burden onto students to “prove their innocence.” In one striking case, the Australian Catholic University acknowledged that Turnitin’s AI tool led to false accusations against students, delaying graduations and causing distress. The institution eventually ceased relying on the tool when used in isolation (Adelaide Now, 2023). Similarly, Vanderbilt University disabled Turnitin’s AI detector entirely, citing concerns about its error rates and lack of transparency (Vanderbilt University Center for Teaching, 2023).

Relying on Turnitin as if it were “infallible” encourages faculty to outsource judgment rather than engage with student writing, context, drafts, and meaning. As a seasoned educator working as a language teacher for over 35 years, relying on AI to do one’s job is an example as to how certain types of teaching professionals degrade pedagogy and erode trust, turning the classroom into a surveillance zone. And what can be said when a paper is submitted to be published in a magazine and it is “checked” for AI generation with Turnitin? Isn’t it another example of outsourcing judgment?

Ethical and Epistemological Objections

At a more fundamental level, what can be said is that policing writing provenance via black-box algorithms is a way to betray an already misguided epistemology. Writing is not a binary product of “human vs. AI,” especially in an era where humans increasingly rely on digital tools such as dictionaries, grammar checkers, and translation aids. The insistence on policing a metaphysical boundary between “just human” and “AI-assisted” is naive and reductive. AI-generated texts are not being “defended” here, the sole intention of this paper is to make teachers help learners use AI to help them with their work such as a cohesion checker, not as a “term-paper producer,” which is unethical.

Furthermore, the ethical consequences of false accusation can be severe: damage to reputation, academic record, emotional distress, and even expulsion. The risk of harm weighs heavily against delegating moral judgment to a fallible system, something that is simply outrageous. And what about those professors wanting to have an article or research paper published where AI was used to improve mechanics, coherence, word choice, data analysis, and so on? Should they be flagged because ChatGPT, Claude.AI, etc. were used?

Recommendations

          From my personal stance, as someone who has been working with English language students predating the advent of AI, these are some of my recommendations to help teaching professionals better cope with AI use in paper or research writing:

 Conclusion

Turnitin’s AI writing detection tool positions itself as a guardian of academic integrity, but in truth it is a blunt, opaque, and potentially prejudicial instrument in the hands of people who simply want to outsource judgment instead of doing their job: “reading the author’s paper.” It is already known that it can be susceptible to false positives, contains what experts have labeled as algorithmic bias, and its misuse in academic adjudication threatens to punish honest non-native writers whose C1 or C2 levels are being questioned. My own experience of being flagged for academic writing composed in 2010, while taking a course at Homerton College, University of Cambridge, starkly illustrates how the system can misfire a false positive disastrously.

Academia must resist the temptation to outsource ethical judgment to algorithms. Until such detection systems become transparent, independently validated, and procedurally constrained, they should serve only as a “flag”, not a “guilty verdict”. The responsibility for assessing writing must rest with human educators in dialogue with writers, not buried in binary scores from inscrutable machines.

📚 References

Adelaide Now. (2023, June 14). ‘Robocheating’ fiasco saw Australian Catholic University students falsely accused of using AI by an unreliable AI tool. News Corp Australia. https://www.adelaidenow.com.au/education/higher-education/robocheating-fiasco-saw-australian-catholic-university-students-falsely-accused-of-using-ai-by-an-unreliable-ai-tool/news-story/4a08732c84499263a709ec3bb1980802

K-12 Dive. (2023, June 7). Turnitin admits there are some cases of higher false positives in AI writing detection tool. https://www.k12dive.com/news/turnitin-false-positives-ai-detector/652221

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers [Preprint]. arXiv. https://arxiv.org/abs/2304.02819

Salem, L., Fiore, S., Kelly, K., & Brock, B. (n.d.). Evaluating the effectiveness of Turnitin’s AI writing indicator model. Temple University Center for the Advancement of Teaching. https://teaching.temple.edu/sites/teaching/files/media/document/Evaluating%20the%20Effectiveness%20of%20Turnitin%E2%80%99s%20AI%20Writing%20Indicator%20Model.pdf

Stanford Human-Centered Artificial Intelligence (HAI). (2023, May 1). AI detectors biased against non-native English writers. https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers

The Markup. (2023, August 14). AI detection tools falsely accuse international students of cheating. https://themarkup.org/machine-learning/2023/08/14/ai-detection-tools-falsely-accuse-international-students-of-cheating

Turnitin. (n.d.-a). Understanding false positives within our AI writing detection capabilities. https://www.turnitin.com/blog/understanding-false-positives-within-our-ai-writing-detection-capabilities

Turnitin. (n.d.-b). New research: Turnitin’s AI detector shows no statistically significant bias against English language learners. https://www.turnitin.com/blog/new-research-turnitin-s-ai-detector-shows-no-statistically-significant-bias-against-english-language-learners

Turnitin. (n.d.-c). Does Turnitin detect AI writing? Debunking common myths and misconceptions. https://www.turnitin.com/blog/does-turnitin-detect-ai-writing-debunking-common-myths-and-misconceptions

Vanderbilt University Center for Teaching. (2023, August 16). Guidance on AI detection and why we’re disabling Turnitin’s AI detector. Vanderbilt Brightspace Blog. https://www.vanderbilt.edu/brightspace/2023/08/16/guidance-on-ai-detection-and-why-were-disabling-turnitins-ai-detector

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., & Waddington, L. (2023). Testing of detection tools for AI-generated text [Preprint]. arXiv. https://arxiv.org/abs/2306.15666

Reader's Handout - Interactive Reading & Reflection Guide by Jonathan Acuña

When Machines Judge Their Makers by Jonathan Acuña