Fernandez, Enrique, PI. Death and Gender in Early Modernity. Database
Bibliographic record
Abstract
How people conceived of death in medieval and early modern Europe is an enormous question.How they distinguished and understood sex and gender in the same period is another very large area of inquiry.Death and Gender in Early Modernity seeks to bring these two questions into conversation with each other by gathering "artistic representations of death, dead bodies, relics, anatomical specimens and burial instructions to analyze how death altered the category of gender in the early modern period" ("Home").In doing so, the site provides a growing collection of images from the period to help scholars work through these important questions.It is a worthy project, and its work is just beginning.As the "Information" page for Death and Gender in Early Modernity articulates, representations of death in the period can be varied-from a body to a relic, a skeleton to a skull, a heart to a hand.Following medieval and early modern theological debates about how a person will be resurrected (with the same gender identity, as a man, as the incarnate Christ had been, or as an unsexed being), images of death in the early modern period suggest that anthropomorphized death, dead bodies, and other material objects symbolizing death may be gendered or un-gendered in different contexts.Death as represented as a skeleton, for instance, can appear to be, in the language of the project, "gender-neutral, " but also can, at times, be clothed or standing in such a way as to suggest a particular gender.Close study of early modern images of death in all its manifold representations, then, can shed light on the extent to which early modern people believed in the continuation of a gendered identity after death.This, in turn, can nuance ideas of how gender identity was constructed in early modern Europe ("About").Death and Gender in Early Modernity is a collaborative project headed by Enrique Fernandez of the University of Manitoba.Sponsored by the Social Sciences and Humanities Research Council of Canada, participants and board members of the project include professors and graduate students from across North America and Europe.Participants may create online collections and exhibits, but it is also possible to join in the conversation even if you are not a
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.
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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.026 |
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".