How community colleges and other TVET institutions contribute to the united nations sustainable development goals
Bibliographic record
Abstract
Even though the importance of technical and vocational education is acknowledged in the Sustainable Development Goals (SDGs) adopted by the United Nations in 2015, the university sector has dominated the discourse on the role of postsecondary educational institutions in sustainability. This comparative study widens the scope by highlighting contributions that community colleges (CCs) and technical and vocational education and training institutions (TVETs) are making to sustainability in several developed and fast-developing countries. It examines five independent case studies – conducted in Canada, Chile, China, Taiwan, and the United States – and demonstrates that CCs and TVETs are uniquely positioned to make substantial contributions and should be an important part of the sustainability discussion. It also explores special features that allow these institutions to play a vital role in addressing the SDGs. The findings show that the SDGs related to economic development and social justice were a priority in all five case studies, while the environmental SDGs were foremost in the two North American studies. The main barriers to sustainable development include the high cost of education, low completion rates, graduates’ inability to secure employment commensurate with their education, inadequate funding and the reputation of CCs and TVETs as second-tier institutions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".