Maneuvering through the treacherous terrains of America’s Colleges/Schools of Education
Bibliographic record
Abstract
Abstract Some aspiring African students prefer to travel “abroad” or overseas for example, the United States, Canada, Germany, France, and other developed countries to further their careers. This was a common practice for well-to-do families. To be more specific, some rich families in Nigeria tend to overlook higher institutions in their country. In my case, my dad inspired me, many decades ago, about travelling overseas to study. I was probably 12 years old when he hinted to me that his wish was for me to study overseas even though he did not mention any particular country. Ever since my dad indicated that desire, I accepted his wish until it came into fruition 20 years later. Specifically, in August 1979, I began my journey to the United States of America. However, while in the foreign land many African students confront multidimensional problems that range from prejudicial perceptions to illusory generalizations. For many Africans, problems include difficulty adjusting to a new cultural environment, xenophobia, misrepresentation, and miscategorization. Despite such problems, they are able to succeed and excel in their chosen professions. In this article, I discuss my experiences while maneuvering the treacherous terrain of America’s Colleges/School of Education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".