Authenticity revisited: The cultural implications of a digital reality
Bibliographic record
Abstract
Abstract UNESCO's “Memory of the World” and Google Print are just two of the many projects that seek to digitize our cultural record. The duplication, preservation, and dissemination of these sources with digital technology, however, come at a cost. The panel will explore the cultural implications of transferring and re‐rendering our historical, cultural, and intellectual legacy in a new medium. In addition to exploring the idioms of digitized informational realities, the panel will also investigate how, recursively, these spaces are effecting change in the analogue world. The re‐appearance of historical artifacts as digitized entities, as well as the emergence of born‐digital entities, has forced us to question our traditional ideas about what constitutes an original or a copy, and what we mean by the term ‘authentic’. Inspired by the success of last year's session, “Authenticity: New Personas for Digital Media,” the panel for 2006 will take a more comprehensive look at the concept of authenticity in both analogue and digital environments. This interdisciplinary panel of established and junior scholars from the humanities and the social sciences will suggest new ways of approaching and understanding emergent informational realities.
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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.104 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".