The Twinning of Potential: Toward an Integration of Prior Learning. Assessment with Career Development. Canadian Journal of Counselling: Toronto: Thompson Promotion of Equality and Prevention of Unfair Discrimination Act (2000). Republic of South Africa.
Bibliographic record
Abstract
This paper argues that prior learning assessment is an integral part of the field of career development. Forging closer ties between the two could have a synergistic effect. The authors highlight some common features in the work of practitioners of prior learning assessment and career development. The purpose of this comparison is to emphasize that commonalties of purpose should not be sacrificed to battles of professional identity. A recent project in the Department o f Human Resources Development Canada, Alberta/Northwest Territories/Nunavut Region exemplifies benefits and issues associ-ated with the integration of these fields. RESUME Cet article démontre que la reconnaissance des acquis professionnels est une partie intégrante du domaine du perfectionnement professionnel. Etablir des liens plus étroits entre les deux pourrait avoir un effet synergique. Les auteurs soulignent plusieurs éléments communs dans le travail des conseillers oeuvrant dans les domaines de la re-connaissance des acquis professionnels et du perfectionnement professionnel. Le but de
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 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".