Bibliographic record
Abstract
In this paper, we analyze some of the platforms and technologies that influence the manner in which we interact and experience historical sites and heritage. Acknowledging that history is a constructed narration of the past, this paper demonstrates how contemporary technologies have agency in reconstructing histories in the present via digital platforms. By comparing online platforms for digital heritage production like Google Heritage with Augmented Reality (AR) and Mixed Reality (MR) platforms, we demonstrate how digital heritage may undergo a process recontextualization or decontextualization from its originating settings. We also show that digital heritage’s reconstruction of history is done through the act of remediation: by turning actual remnants of the past into digital models or by replacing such remnants with virtual representation that are globally accessible, something new is created and alternative stories can be told. Within that, we consider some of the ethical issues that are raised by the migration of historical narratives into digital platforms, as we point towards a growing tendency in which history and its production can be subjected to major data companies.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".