Job Developers in Transition: A Study of Informal and Nonformal Job Skills Training of Job Developers in Nonprofit Organizations in Ontario
Bibliographic record
Abstract
Job Developers have complex and demanding jobs that require balancing the needs of organizations, employers, and job seekers. Job Developers must meet new employers and potential employees every day, earn their trust, and learn their needs. A common role Job Developers play is helping people find jobs and helping employers find employees. Job Developers attempt to learn what employers and job seekers need and what each can offer to match the right applicants to the right employers.Competent Job Developers must have organization, research, marketing, selling, communication, and negotiation skills. Job development has become a high growth occupation. Because the nature of their jobs changes constantly, Job Developers must also stays updated on employment trends and labor market information. While these changes provide opportunities for practitioners to expand their roles, they also impose increased demands and challenges to build their skills and capacity to perform their jobs. The job developer profession (also known as employment specialist) is a recently new concept in the nonprofit sector. Job Developers' potential as advocates for the unemployed, those with disabilities, and new immigrants is fundamental in today's competitive job market and in the context of equitable opportunity for employment. Informal and nonformal learning are well-recognized and well-used in the job development field. Job Developers rely on informal and nonformal learning for professional development and occupational autonomy.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".