Authenticity: New personas for digital media
Bibliographic record
Abstract
Abstract This panel approaches the problem of authenticity as the concept is evolving through technological practices. Most debates surrounding this evolution have proceeded from a naturalized definition of authenticity. This definition is largely founded on the printed word tradition and the principle that faithfulness to the written word is paramount, while stylistic modifications to the form of documents are acceptable. This naturalized definition can be problematized using two different axes: (1) from textual to non‐textual (e.g., audio‐visual) documents; (2) from paper to electronic media. In the first case, there are no general and stable conventions for distinguishing content from its formal manifestation. In the second case, the only stable measure of authenticity, that of bitwise integrity, (a) is too restrictive to deal with the inevitable logical format migrations that must occur for digital media to remain accessible; and (b) does not measure how the rendering process of the bitstream (e.g., on screen, on paper) conforms to the content of the document. The papers in this panel will explore how new electronic documentary practices challenge the naturalized definition of authenticity, and chart how concepts of authenticity evolve in conjunction with such practices. The papers will suggest how new rules and conventions for defining authenticity may emerge in given areas of documentary practices — digitized medieval manuscripts, electronic contracts, XML‐encoded documents — or from multidisciplinary research efforts.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".