Why FRBRoo and CIDOC CRM are great for expressing (Linked, Open) Ethnographic Research Data
Bibliographic record
Abstract
Can we use a Linked Open Data framework in place of classic static metadata in a Fedora Repository? We describe how we catalogued a collection of interrelated ethnographic research data using the FRBRoo and CRM ontologies. These vocabularies allowed us to express detailed relationships between digital objects and entities (people, places, events, concepts) in a more nuanced way than traditional bibliographic metadata schemas such as MODS and MADS. Our implementation uses a network of entities in a Fedora repository, with the CRM and FRBRoo properties in the Mulgara triplestore, to catalogue data from an ethnographic project in a way that will drive the Islandora display, while allowing the network to be queried by researchers. We describe the process of transforming our data set, consisting of audio and video recordings; photographs; biographical information; music notation files; and textual content, for inclusion into this RDF-centric repository, and the challenges encountered in modelling born-digital content in vocabularies designed to contain surrogates for physical objects.
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 | 0.103 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.031 | 0.060 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".