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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".