Mapping national research profiles in social science disciplines
Bibliographic record
Abstract
The paper investigates the advantages of graphical mapping of national research publication and citation profiles from scientific fields in order to provide additional information with respect to research performance. By means of multi‐dimensional scaling techniques national social science profiles from seventeen OECD countries and two periods, 1989‐1993 and 1994‐1998, are mapped, each profile represented by a vector of either publication volumes or citation values for nine social science fields. Aside from demonstrating the developments of publication volumes and citedness ranges as well as patterns, the graphical maps display clusters and similarities of national profiles over time. Combined with international rankings of averaged national impact factors (NIF) relative to the average world impact of field (WIF) for the same number of fields and periods, the graphical display supplies additional otherwise concealed information of the differences in research patterns between countries – even when the NIFs are quite similar. The analyses show that low Pearson correlation coefficients can be applied to flag extraordinary instances of either high or low national citation impacts during a period. Most importantly, the graphical maps make a strong case for adjusting or tuning the baseline impact to the actual national publication profiles when comparing NIFs of different countries. A new indicator, the Tuned Citation Impact Index (TCII) is proposed. It is constructed from the amount of expected citations a country ought to have received in each research field aggregated over its true profile. Common baseline profiles, like those of the world or EU, are consequently not regarded as the ideal benchmark. In the case illustrated by the journal publications of the social sciences the paper verifies the hypothesis that a dominant central cluster exists consisting of the large Anglo‐American countries: USA, Canada and the UK. A further hypothesis, that the smaller northern EU countries with English as the second language are located together and close to the central cluster on the publication maps is only partly satisfied in the second period. A third hypothesis, that countries located near the central cluster on the citation maps may hold high(er) NIFs is falsified.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".