Funding and its impact on the administration and organisational efficiency of technical vocational education and training institutions
Bibliographic record
Abstract
Abstract In the present study, the author examines how the lack of funding can impact the administration and the organisational efficiency of technical vocational education and training (TVET) institutions (I). The research examines human capital development and TVET as instruments for propelling growth and development and contributing to Trinidad and Tobago’s (T&T) economic and social advancements. The study explored how TVETIs can be a central pillar in the progress of T&T’s development. The research material consists of 10 interviews with educational experts from four postsecondary TVETIs in T&T and the chief executive of T&T TVET’s regulatory body. Focus groups and individual interviews were conducted with the leading regulator, instructors, managers, and a curriculum expert. The author utilised thematic analysis in the research by adopting a deductive approach. The data revealed several challenges that engulf the administration and management of TVETI in T&T, mainly because of financial woes. Three themes emerged from the data: Funding for Administration, Funding for Human Capital Development and Funding for Organisational Efficiency. Insightful revelations were unearthed from the educational experts, which were generated from the data which provide an in-depth perspective on the administration and management of TVET in T&T. The results show that T&T’s education experts see funding as a significant challenge. Many argue that a lack of funding severely impacts their institutions, which erodes human capital development growth. The study concludes that a lack of funding can severely and has impacted TVET in several ways. An absence or a lack of funding brought the closure of top-notch schools used in the training and growth of teachers for the educational system. Human capital development and TVET were also highlighted as essential tools for economic and social deprivation. However, a better approach is needed to finance TVET moving forward.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".