Exploratory Study of Talent Management and Information Technology in Canadian Nonprofit 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
We examine talent management in the Canadian nonprofit organizations and explore how talent management is defined and practiced in Canadian nonprofit information technology departments. Individual depth interviews were used to collect data. A critical realist approach to interviewing was used in this explorative study. The results indicate that Canadian nonprofit IT decision makers have a unique view of talent management that differs in many respects to those described in the academic literature. The participants in this study tend to focus on recruiting, identifying, and developing internal pools of talent, rather than trying to compete in a “war for talent” in the external job market. This study contributes a new perspective on talent management by providing empirical insights from outside the US and for-profit context with implications for the broader discussion, conceptualization, and practice in the field.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it