Bibliographic record
Abstract
ABSTRACT This article deals with the use of language in historiography and with this usage's implications for the conception of history and the historiographical operation/practice. Whereas theorists of “presence” believe that “presence” and “reality” can be grasped in spoken language and written texts, thus generally considering them as a medium that enables access to a “reality” that lies beyond texts and language, I argue that language and texts should themselves be considered as a “reality.” We need to distinguish the process of “presentification” performed by words from the presence of language as a lexical and physical reality; though the two aspects are strictly connected, the presence of language needs to be emphasized as a lexical‐semantic system and as a thing in the world. In this article, I consider language as a “living witness” of the narrated events; it is a presence in the moment that events occurred and a presence that is still present. We should think of language as we think of the material world around us—that is, as a transformed landscape that contains present and absent pasts. Historians of “presence” consider the meanings associated with language as a major obstacle obstructing the understanding of history in a new unmediated way; to some extent, this article is an attempt to hold meaning and presence together.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".