Goldener, grüner und "anderer" Weg zu Open Access: Statistiken für Österreich
Bibliographic record
Abstract
Archambault et al. veröffentlichten 2014 einen Report, der die Open-Access-(OA)-Raten für Publikationen der Jahre 2008–2013 aus allen Ländern des Europäischen Forschungsraums (ERA) sowie für Brasilien, Kanada, Japan und die USA enthält. Unterschieden wird dabei nicht nur nach Disziplinen, sondern auch nach drei OA-Kategorien: Gold, Grün und einer Restkategorie. Einige wichtige Ergebnisse der Studie werden zusammengefasst und die Daten für Österreich mit jenen des untersuchten Gesamtraums verglichen. Im Ergebnis zeigt sich, dass Österreich in fast allen Disziplinen überdurchschnittlich hohe OA-Raten aufweist. Der Fachbereich „Philosophy & Theology“ bildet dabei jedoch eine Ausnahme. Außerdem scheint das Zugänglichmachen über Soziale Netzwerke und persönliche Homepages in fast allen Fachbereichen beliebter zu sein als der Goldene oder der Grüne Weg. Das gilt insbesondere für die Geistes- und Sozialwissenschaften. Die Daten der Study of Open Access Publishing (SOAP) lassen darauf schließen, dass es OA-Journals in diesen Disziplinen noch an Reputation mangelt.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open scienceBibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".