Bibliographic record
Abstract
Turkey's recent collaborative and genuine contributors to medicineThe overemphasis of economic growth globally and, particularly, in Turkey in the past quarter of a century has not only exposed its limitations regarding the environment, resources, and global warming but also led to the neglect of sound policies in education and scientific research.A main consequence that emerged has been the so-called "middle-income country trap" that Turkey has been experiencing in the past 6 years.Scientific research is indispensable for human development, as well as export-led economic growth.The UN Development Program recently published its traditional index for 2013, which uses three basic dimensions: longevity in conjunction with a healthy life, access to knowledge, and a life standard appropriate to human beings.Thus, two of the three dimensions are intimately woven into knowledge, science, and medicine.Among 187 countries ranked in the Human Development Index, Turkey is placed no better than 69 th , behind Libya, Malaysia, Lebanon, Belarus, and Venezuela.Assessment of the international position of a nation is vital for the decision of scientific priorities and support by the government, business circles, and foundations (1), and indicators of scientific activity shed light on the appropriate disposition of national resources (2).As the most reliable indicator of scientific contributions, highly cited papers, such as the top 1% of all articles with the highest citations in a given field, are generally held to better reflect the contribution (1).The real contribution of the individual author, institution, or country is diluted among the growing proportion of publications with international collaboration, which include clinical trials, meta-analyses, guidelines, or scientific statements.These internationally "collaborative" papers receive high numbers of citations, thus requiring separate consideration, while the "genuine" contribution of the native researcher in such papers may be near-negligible.A method previously proposed by this author-namely, assessing research that attains citations above a relatively high threshold
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".