El canon literario hispanoamericano en la era digital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, I propose an alternative technique to the traditional method of constitution of the literary canon. Instead of basing the determination of the canon on different values and criteria, I scrutinize the Modern Language Association International Bibliography database in order to determine the most cited authors and literary works. Specifically, I study Spanish American literature. Thus, through the process of data mining, I obtain a sample of over 75,000 references that allows us to observe the critical bibliography about the nineteen national literatures of the subcontinent. This quantitative technique yields a corpus of 451 titles and 717 writers that are cited more than 100 times in the database. Consequently, this bibliography is not the result of subjective selection criteria, but is based on the law of large numbers. Furthermore, this study shows that the quantitative analysis of bibliographic digital databases is an effective way to bring new light to the field of literary studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it