An Ontology of Semiotic Activity and Epistemic Figuration of Heritage, Memory and Identity Practices on Social Network Sites
Bibliographic record
Abstract
This study presents the construction and validation of a formal conceptual model, or domain ontology, useful for the formal representation and analysis of conversations on heritage, memory and identity (HMI) on social network sites (SNS), of interviews with participants in such conversations, and of scholarly works engaging with such phenomena. The ontology provides for the first time a conceptual framework for HM interactions on SNS addressing the semiotic and discursive nature of such interactions in the context of cultural-historical activity theory and semiosphere theory. Part of the Connective Digital Memory in the Borderlands research project, it is developed using an evidence-based knowledge elicitation and domain modeling approach. The study presents the three components of the ontology: an event-centric core conceptual model, an inductively derived concept taxonomy, and a meta-theoretical conceptual scheme, based on a combination of conceptual analysis and lexical analysis of relevant scholarly literature. To validate the ontology, it then provides an example of how it can be used to represent an actual HMI-related SNS conversation and scholarly intervention using knowledge graphs, a quantitative analysis of the occurrence of taxonomy terms in different subfields of HMI on SNS studies, a qualitative analysis of concepts used in studies on non-professional, archeological, and institutional heritage communication on SNS, and a meta-theoretical account of studies of HMI on SNS. The ontology can be used as a framework for theorization and for the development of data models, questionnaire protocols, thematic analysis vocabularies, and analysis queries relevant to HMI on SNS research.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".