<A> and <B>: Marks, Maps, Media, and the Materiality of Ambrose Bierce’s Style
Bibliographic record
Abstract
It is official: the “digital ” has tagged the “humanities” and there is no turning back. New media have marked up the tag cloud of American literature and reprocessed the words, tools, methods, and metaphors of scholarly markup. So be it. “Media determine our situ-ation ” (Kittler 1999, xxxix). This is not a one-way ticket to techno-logical determinism but a round-trip pass that brings us through and beyond the historical conditions of our ongoing markup. Feel free to click away and hit escape, but digital media operate faster than the tap of your fingers. Better to dwell in the middle of things where marks, maps, and media continue to inscribe our scholarly condition, read-ing and writing the ever-present scene that invites our human touch. “For what we know and what we have known are ongoing, ” Jerome McGann (2013, 334) writes, and so let us make haste to “preserve, monitor, investigate, and augment our cultural inheritance, including all the material means by which it has been realized and transmitted.” Even the ones that make us wince, rescaling the hallowed ground on which we stand. So why Ambrose Bierce? Why tag him? My short answer is because he tags us, reprocessing words not as “signs of natural facts, ” as Emer-son ([1836] 2000, 13) described them, but as discrete marks, media, and properties. All words were fighting words for Bierce, and the let-ters he manipulated on paper continue to perform complex operations that mark up the digital with the cultural techniques of writing’s past. As “new media ” throws print into relief, it is imperative that we exca-vate the “future of the literary past ” by reading the marks, media, and techniques that historicize the language and logic of markup (McGill
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.027 |
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".