Building bridges: mapping diverse classifications for a seamless user navigation experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes a BBC project to unify Archive and Production workspaces, during<br> which numerous issues with managing different types of metadata and Knowledge Organi-<br> sation Systems (KOSs) were encountered. Integrating diverse content silos requires bringing<br> together not simply the assets, but also the metadata used to manage those assets. The paper<br> summarises the theoretical background to the project, the BBC’s ‘information ecosystem’,<br> and the user research and requirements-gathering exercises undertaken.<br> Much work on developing metadata crosswalks has been at the heading or label level, and<br> not based on semantic analysis of the content of the labelling or description. However, such<br> semantic analysis needs to be undertaken when mapping diverse taxonomies, thesauri, and<br> keyword lists and, in practice, often needs to balance preservation of local or specialised<br> terminology with accessibility for general users. Just as metadata about content permits the<br> organization of that content, so metadata about metadata (parametadata, or meta-metadata)<br> permits the organization of metadata, enabling end users to make informed browse and<br> navigation choices. Increasingly, in order to integrate content, different KOSs, such as<br> taxonomies and ontologies, need to be related.<br> The paper concludes by summarising the ways in which problems that arose during the<br> integration project were resolved, and how policies for managing parametadata, subjective<br> metadata, and semantic-level mapping were developed.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it