Bibliographic record
Abstract
Archivists and librarians play a critical role in preserving and making accessible cultural resources, but there is now an uncertainty as to whether their traditional expertise is sufficient when dealing with digital resources. A particular focus of concern is the authenticity of these resources. This article looks at how the concept of authenticity has been constructed in traditional environments, and specifically by philosophers, art conservators, textual critics, judges, and legislators. It is organized around three broad definitions of authenticity: authentic as true to oneself; authentic as original; and authentic as trustworthy statement of fact. The examination of these definitions of authenticity and their interpretation in different contexts suggests that authenticity is best understood as a social construction that has been put into place to achieve a particular aim. Its structures and goals vary from one field to the next and from one age to another. The article concludes that digital resources are comparable to traditional cultural resources such as art works, literary texts, and business records; they are in a continuous state of becoming and their authenticity is contingent and changeable.
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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.110 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".