From Heroic Science to Visual Studies: Pathways of Heraldry among Concepts, Images, and Contexts
Bibliographic record
Abstract
The relationship between heraldry and academic research, especially in university context, has generally been neither easy nor straightforward.The reasons undoubtedly stem from various factors, but the core of the issue was summarized by Faustino Menéndez Pidal: 1 stated in general terms, [heraldry] has not always had a good reputation-partly for good reason, as much of what has been written under this name does not merit scientific consideration, and partly without justification, as the disregard attributed to the texts should not have been extended to the subject itself.However, the excessive focus on matters of little or no interest and the numerous misguided interpretations led some to believe that achieving better results in this field was impossible."2 It is, therefore, a matter of historians' prejudice toward heraldic studies or heraldists; and, as a reaction and compensation, the latter's splendid isolation from the academic world-with honorable exceptions on both sides.Upon examining the reasons behind this mutual prejudice, it becomes evident that they largely stem from the way heraldic knowledge has been constructed until quite recently (if not to this day) by heraldists themselves.This image portrays heraldry as 1
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".