Waiting for RiC: The Release of Records in Contexts, version 1.0
Bibliographic record
Abstract
Waiting for RiCto archival description, their attributes, and their relations. 3 Records in Context -Ontology (RiC-O) provides rules for translating the model's entities, attributes, and relations into the classes and properties of a web ontology language (OWL) that will allow archival descriptions to be published as linked data on the Web. 4 Records in Context -Foundations of Archival Description (RiC-FAD) is a brief narrative introduction to the system. 5The last piece is titled "Application Guidelines" (RiC-AG); this has not yet appeared, and EGAD has only just started work on it.Finally, RiC's replacement of the existing standards is not a straightforward substitution.RiC has radically recast the standards' structure, such that "archivists familiar with ISAD(G) may initially find RiC-CM challenging to understand." 6 Its adoption will be dependent on software that largely does not yet exist.RiC's authors foresee a gradual transition period, in which the existing standards continue to be used as both archivists and software developers find their way with the new standard. 7 The purpose of this communication note is to help clarify some of the issues likely to be encountered along that way: What is RiC, how does it differ from previous standards, and what does it all mean for Canadian archivists still lumbering along with the Rules for Archival Description (RAD)?This note provides some background, exposition, and commentary, and it suggests some practical ways by which archivists can starting using RiC without adopting it (in the absence of RiC software).The tone is somewhat tentative throughout.It is difficult to assess RiC without working through the weeds of testing its model against existing descriptions or seeing its linked data implementations in action -two tasks that still largely belong to the future.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".