The Battle of the Widows: <i>La Montálvez</i> versus <i>Clemencia</i>
Bibliographic record
Abstract
Their liminality in a patriarchal society and the cultural apprehensions surrounding widows explain why literature has rarely been sympathetic to them. This has been particularly the case with the seductive ones, the viudas alegres who are perceived as a threat to the social order. With a didactic and ideological purpose in mind, José María de Pereda condemns Madrid’s viudas verdes in La Montálvez (1888), while in Clemencia (1852), Fernán Caballero combats the backlash against widows by creating an impeccable role model and representative of Spain’s periphery. This article reads both novels as counternarratives of widowhood and examines how their authors develop two very different narratives to control and confine female excess –sexual in one case, intellectual in the other– within a conservative gender and national paradigm.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".