What inspires Career Professionals in Ontario's Non-Profit Employment Agencies to Remain Intrinsically Motivated?
Bibliographic record
Abstract
This exploratory case study sought to find out what motivates career professionals in Ontario’s non-profit employment agencies to reach and exceed their pre-set targets. Unlike profit-earning organizations, career professionals of non-profit employment agencies in Ontario do not get any additional financial incentives for exceeding their targets of helping job seekers find sustainable employment. In this study, seven mid-level managers and seven career professionals of non-profit employment agencies were interviewed. The research used a transformative learning theory lens (Mezirow, 1991), and also an interpretivist framework (Merriam, 1998) to understand the data. A semi-structured interview format was used for the oneon-one interviews. Additional data were collected via document perusal, field notes, and the researcher’s reflective journals. The data were coded and analyzed thematically using a content analysis method. Triangulation and member-checking were performed for ensuring reliability of data (Yin, 2009). The study suggests that the career professionals of the seven non-profit employment agencies are by and large, intrinsically motivated, and three of their key motivators are “passion for their jobs”, “empathy for the clients” and “changing other people’s lives” in a positive way
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".