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
By far the most popular specification for learning objects is the IEEE Learning Object Metadata (LOM) standard. In it are outlined 76 different elements that correspond to pedagogical, technical, and administrative aspects of learning objects. This standard, however, has proven to be ineffective for creating computer adapted dynamic courseware. This paper outlines some initialr esear ch we ar e d oing in acquir ing, descr ibing, and using lear ning object metadata. Instead of the IEEE LOM, we ar gue for a mor e flexible appr oach to both defining and associating metadata with lear ning objects. By cr eating domain, educational, and lear ner char acter istic ontologies, content can be dynamically linked to those competencies that ar e o bser ved in ar unning e-lear ning system. This pr ovides for a set of evolutionar y me tadata, wher e softwar e agents can inspect multiple metadata instances for a given lea r ning object andr eason over them for a par ticular goal. As more metadata instances are added to the system, agents are expected to be able to provide more accurate reasoning, eventually leading to the dynamic delivery of personalized course content.
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 | 0.000 | 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) | 0.012 | 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 it