A dynamic community of discovery: Planning, learning, and change
Bibliographic record
Abstract
Ryerson University’s Prior Learning and Competency Evaluation and Documentation (PLACED) program is funded by the Government of Ontario to engage internationally educated professionals (IEPs), employers, and regulatory/occupational bodies in the use of competency-based practices. In 2008, the authors created a self-assessment tool for IEPs that would build a portfolio reflecting an individual’s knowledge and skills while introducing him or her to aspects of the Canadian workplace and labour market. The authors felt that this tool would be useful to assist IEPs in considering their career options and wanted to create an online workshop that would provide flexibility to users whose priorities were most likely work and family obligations. This short project description will capture a) why the self-assessment tool was developed; (b) how we fostered participants’ self-efficacy; c) how we used Blackboard; (d) what the participants gained from the workshop; and (e) how the workshop has evolved based on facilitators’ observations, participants’ feedback, and an external organization’s request for customizing the workshop. In working together to design the online workshop, IEPs’ Self-Assessment and Planning, we focused on two main concepts: self-assessment and career planning. With that in mind, we set out in the workshop to bolster self-discovery, self-efficacy, individualized research skills, action planning, and ongoing professional development. The learning platform was Blackboard, which is used across Ryerson University in both classroom and online learning.
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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".