Building bridges: mapping diverse classifications for a seamless user navigation experience
Bibliographic record
Abstract
This paper describes a BBC project to unify Archive and Production workspaces, during which numerous issues with managing different types of metadata and Knowledge Organi- sation Systems (KOSs) were encountered. Integrating diverse content silos requires bringing together not simply the assets, but also the metadata used to manage those assets. The paper summarises the theoretical background to the project, the BBC’s ‘information ecosystem’, and the user research and requirements-gathering exercises undertaken. Much work on developing metadata crosswalks has been at the heading or label level, and not based on semantic analysis of the content of the labelling or description. However, such semantic analysis needs to be undertaken when mapping diverse taxonomies, thesauri, and keyword lists and, in practice, often needs to balance preservation of local or specialised terminology with accessibility for general users. Just as metadata about content permits the organization of that content, so metadata about metadata (parametadata, or meta-metadata) permits the organization of metadata, enabling end users to make informed browse and navigation choices. Increasingly, in order to integrate content, different KOSs, such as taxonomies and ontologies, need to be related. The paper concludes by summarising the ways in which problems that arose during the integration project were resolved, and how policies for managing parametadata, subjective metadata, and semantic-level mapping were developed.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".