Digital betrayals: "translating" the ancient drawings of the Monastery of Nossa Senhora da Rosa
Bibliographic record
Abstract
The research described in this paper used digital technologies to study the ethical and philosophical implications related to the analysis and interpretation of historical sources of cultural heritage. As the described technologies used are not neutral tools, they actively shape heritage narratives, influencing how we perceive and represent the past. Thus, reconstructing the past through heritage fragments cannot be objective or universally accepted, as it is inherently shaped by contemporary social, cultural, and political contexts. The past, therefore, can only be analysed from the unique perspective of the present. In this regard, the American historian David Lowenthal suggests “the past is a foreign country,” and therefore, its language, customs, and traditions cannot be fully understood. Since heritage is irreconcilable with the past, or more precisely —quoting Lowenthal again—since heritage is a “distortion” of the past, cultural heritage studies should combat those simulacra that define the past as objective, unambiguous, and unquestionable—definitions often protected by academia. The case study this paper focuses on is the Monastery of Nossa Senhora da Rosa of the Order of São Paulo da Serra de Ossa (Caparica, Portugal). It represents an opportunity to reflect on the ambiguity implicit when reading historical sources, always involving a certain interpretive and creative component. Borrowing the semiotic concept of translation, it is possible to say that reading any source is comparable to translating a document from an obscure and mysterious “language”, that of the past, to the contemporary world “language”. During the process of transferring word meanings from one language to another, many nuances inevitably end up being twisted, lost, or otherwise altered. Consequently, “translating” the drawing into the real building (as well as into a virtual replica) is, to use a famous 16thcenturyanalogy, a sort of “betrayal,” a departure from the initial idea represented on the sheet. This is particularly pertinent when the building no longer exists, as is the case with the Monastery of Nossa Senhora da Rosa. What are the limits of interpretation, its criteria, and the freedom that the "reader/interpreter" can take? What is the role of digital technologies in this theoretical framework? This research, rather than extolling the photorealistic results of digital heritage visualisations and their uniqueness, focuses on the process that led to certain interpretations, emphasising their subjective and context-dependent aspects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".