Bibliometric Analysis of Major Neurosurgical Publications 2011"&#".ord($0).";""&#".ord($0).";""&#".ord($0).";"2020, Part 2: Journal, Author, Yearly Publication Trends, and Citation Related Metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Scientometrics, a subfield of bibliometrics, examines scientific publications by using bibliometric methods. The aim of a scientometric study is to study the various citation-based metrics of scientific articles, such as parameters pertaining to authors (including institutions and country of origin), articles, journals, and other citation related metrics. Objective: , and Methods: We analyzed parameters, including article and journal metrics (total articles published per journal per year, breakdown of the Bradford's law distribution of journals, and Lotka's law, journal impact factors), author metrics (country of origin, collaborations), citation totals, and keyword counts. Results and Discussion: , which experienced a mild decline in 2020. Canadian authors were the most likely to participate in multi-country collaborations. Conclusion: .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.681 | 0.865 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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 it