Bibliographic record
Abstract
Abstract The concept of remediation, as elaborated by Jay David Bolter and Richard Grusin in Remediation: Understanding New Media (1999), is premised on the notion that media are best understood in interaction rather than in isolation. Every artistic medium, they argue, orients itself in relation to another medium, whether respectfully—as in the case of an online literary database that seeks to provide easy access to faithful facsimiles of manuscripts—or competitively—as with a videogame that seeks to replace the linearity and passivity of print with open-ended interactivity. Bolter and Grusin describe individual media in turns of two basic impulses: immediacy, or the attempt to erase the mediating function and present the illusion of directly represented reality; and hypermediacy, or the attempt to foreground the mediating function, exposing the impossibility of direct representation. They employ the same vocabulary to describe the interaction of media. Every act of remediation—every representation of one medium in another—necessarily involves both immediacy and hypermediacy. A digital edition of a literary text grants access to the words of the original print artifact (immediacy), yet by including audio readings and video commentary draws attention to its digital-specific affordances (hypermediacy). A digital archive gathers together high-resolution, color-accurate reproductions of materials scattered in rare-book libraries around the world (immediacy), yet by granting free and instantaneous access to these precious, fragile objects, fundamentally transforms the experience of engaging with their analog originals (hypermediacy). Insisting that one approach media through interaction, Bolter and Grusin’s theory of remediation positions the movement of content from one medium to another as a form of translation—a transformative act in which much is lost as well as gained.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.185 | 0.045 |
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".