The validation and brokering of competence: Issues of trust and technology
Bibliographic record
Abstract
Campus Canada promotes lifelong learning through the articulation of workplace and other experiential learning for academic credit. This paper describes the recent re-development of the Record of Learning (RoL) component of the Campus Canada's e-Portfolio System. In matching the academic members' desires for provision of secure e-transcripts with learners' desires for validation of educational assertions, the RoL provides decentralized secure service without creating clerical log jams. The new RoL system paves the way for web services interoperability between registrar services and for automated creation of secure Records of Learning by workplace trainers. Further development is foreseen to establish the RoL as a separate service interoperable with a variety of e-portfolio and credentialing agencies.
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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 | 0.070 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".