Bibliographic record
Abstract
This research focus on the role of maker- space to promote Stem education in Nigeria, The target audience for the work is learners that are at the stage of learning who are involve in invention creation . Makerspace can be conceived as an avenue for inventors to use their inbuilt talents to bring up an innovation. The word STEM mean Science, Technology, Engineering and Mathematics a broad term from different disciplines put together under an umbrella of the word . STEM is a ubiquitous word that cuts across every facet of human existence beginning with conception, birthing process, activities that sorround growth and development for the realization of goals and human development in invention exercise.
 STEM education is germane to the survival of every developed, under developed and developing nation hence the look at the state of STEM education globally, in the United State of America for instance it has helped her citizen to build a formidable education, health care, economy, social amenities and military system that drawn her strength from STEM education while in in India, Italy and Singapore, we have homogenous international set of comprehensive STEM standards and education for schools based on the demography, availability of resources, qualifications of facilitators and experiences of STEM educators.
 While in Canada the youths disengage from Science, Technology, Engineering, and Mathematics (STEM) studies before graduation from secondary schools, this is detrimental to the growth of the nation to therefore address the nation retraced its steps, determined to prepare the youth for a future where disruptive technologies and changes in the labour market will reward highly skilled workers. While in Africa the continent is lagging behind other continents of the world in scientific productivity and knowledge systems.. To achieve the expected the stakeholders the decision makers that focus on education of Africa must be willing to embrace STEM education in its totality and must be willing to make needed resources available for the development and growth of education in African continent.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".