Bibliographic record
Abstract
There are certain works of fiction that one repeatedly revisits. I, for instance, find myself constantly going back to the short stories of Mario Benedetti (Uruguay, 1920). What fascinates me about Benedetti's short fiction is the way he manages to include the uncanny and unusual in situations that appear to be common and ordinary. Routine seems undisturbed in Benedetti's fictional worlds, even when it is immersed in a sort of enigmatic horror. Such routine is depicted in Benedetti's story Musak (1965). In this story, readers are presented with the perils of monotonous life in an office. This is, no doubt, an old tale. Yet the deceptive simplicity of Musak conceals several other layers of interpretation. Through his unreliable narrator, Benedetti invites his readers to consider, among other things, the boundaries that supposedly separate the sane from the mad, the language that we use, the way we decode the language of our interlocutors, and the lack of awareness that we may have about our own condition. In this paper, through a close reading of the story, I explore the different layers of interpretation that Musak entails, and I suggest that, after nearly forty years, Benedetti's pondering remains insistently current. Mario Benedetti is one of the most productive writers in the Spanish-speaking literary world. He has published essays, plays, poems, novels, and short stories. He actually excels in the short story, most likely because he is--as he describes himself--a poet who writes short stories. (1) In his several collections of short fiction, Benedetti portrays the middle-class montevideanos, either in their city--Montevideo--or in exile in a foreign country. Throughout his literary works, Benedetti has created a fictional Uruguay in which characters and stories offer a vivid representation of the overwhelming changes in Uruguayan history during the last five decades. (2) As in the real Uruguay, in Benedetti's Uruguay, characters adopt different idiosyncrasies and behaviors, and they have different hopes, expectations, fears, and anxieties. What has not changed is Benedetti's choice to write about the middle-class montevideano. A space that is shared by the middle classes in all cities is the office. Accordingly, the office is a common location for the action in several of Benedetti's works of fiction. This has also been a major theme in his essays, especially during the 1950s and 1960s, when he was an earnest critic of life in the public office. Office work, Benedetti claims, provides for a safe and secure future, but oficinistas (office workers) pay a high cost, for the office has the power to deplete the minds of those who work in it to the extent that their thoughts and emotions are consumed. They lose their freedom and lack the energy to regain it. What is worse, Benedetti states, is that they are completely unaware of their own condition. (3) As Corina Mathieu points out, several of Benedetti's characters end up being victims of the office. (4) Musak illustrates the victimization to which Mathieu refers. Benedetti, in this short story, warns us that an office in a newspaper is, after all, still an office. Workers here run the same risks and face the same dangers as those in any other office. In other words, work in an office alienates individuals from themselves and from each other to the point of making them oblivious to reality. Even worse, people may lose all capacity to be critical and therefore may end up gladly accepting whatever situation arises, either favorable, or, more likely, damaging. Yet, Musak suggests much more than that, as I have previously stated. Before undertaking any interpretation, though, I prefer to offer a summary of the story. The action in Musak takes place in the office of a daily newspaper where reporters work listening to musak. It is apparent that they have no choice over what music is played or whether or not to turn it off altogether. …
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Literary close reading of a Benedetti short story; literary criticism.
This provides a literary interpretation of a short story, not a study of research.
Literary close reading of Benedetti's short fiction; humanities criticism, not research systems.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".