They Want to Be Global Citizens: Now What?: Implications of the NGO Career Arc for Students and Faculty Mentors
Bibliographic record
Abstract
Once faculty have inspired their students to want to become Global Citizens, many of these students will approach them for advice about careers that will enable them to live out their commitment to global justice. This article seeks to inform such discussions by providing students and their faculty mentors with information to help consider whether the NGO sector is a good fit for the student, how to prepare for it, and how to advance within it. It does so by providing a snapshot of the nonprofit/NGO career arc based upon analysis of 220 responses to a survey conducted in 2010 of staff of ‘NGOs that advance human rights’ located in Ontario, Canada. Topics discussed include: the importance of when people take an interest in the sector; the relationship between campus clubs and volunteering and NGO careers; the importance of the BA versus the MA to employability; the typical career pattern; what recent entrants might learn from more established staff; types of specific occupation in the sector; how executive directors differ from other staff; and patterns related to gender within the sector.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".