Revolution of experiences: evolution of the skills and knowledge profile
Bibliographic record
Abstract
The Skills and Knowledge Profile (SKP) as presented in this paper is a tool intended to document learning styles and strategies of adult learners. The stated goal of researchers was to develop a systematic approach to capturing the learning of unemployed and employed adults across sectors. In order to develop a user-friendly utilitarian SKP they adopted an action based research method, engaging learners in a unionized factory, community-based women's employment program and community-based literacy program. Volunteers in all the three sites committed their time and efforts to filling out the SKP and then provided us with feedback on the clarity, usefulness and ease of the tool. The paper documents the evolution of the SKP from its inception in the spring of '97 to the end of the '98. The SKP is shown to have traveled through the hands of learners in Ontario and British Colombia and workshop participants in Montreal and Toronto all of which have offered feedback that has made the final product significantly different than the original version. The SKP has experienced revisions on three fronts: the text and format of the SKP; the method by which it is administered; and the purpose of the SKP
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".