ERIC ED477403: Skills and Training in the Non-Profit Sector. CPRN Research Series on Human Resources in the Non-Profit Sector.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Training in Canada's nonprofit sector was examined through a review of data from Canada's Workplace and Employer Survey, which collected data from a nationally representative sample of Canadian workplaces and paid employees in those workplaces. Overall, 61% of employees in nonprofit organizations considered a postsecondary credential necessary to do their job (versus 36% of employees in the for-profit sector and 70% in the quango sector, which was defined as nonprofit organizations in "quasi-public" industries). About half of employers in the nonprofit and for-profit sectors reported increases in skill requirements since beginning their current jobs. Employers in all three sectors rated the importance of increasing employee skills highly. Nonprofit organizations were more likely to provide training for their employees than for-profit organizations were. Training in the for-profit sector was more likely to consist of on-the-job training. Women and employees aged 35 or older in the nonprofit and quango sectors were much more likely than their for-profit counterparts to have received training in the previous year. Thirty-six percent of employees in the nonprofit sector and 38% in the quango sector stated that they received too little training for the demands of their job (versus only 27% of employees in the for-profit sector). (Twenty-five tables/figures are included. The bibliography lists 21 references.) (MN)
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.001 |
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 it