Pacific Conservation Biology: an authorship and citation analysis
Bibliographic record
Abstract
We analysed Pacific Conservation Biology?s authorship and readership from 1993?2007 to quantify who publishes in the journal, who cites the journal, how the journal compares to other conservation journals and whether there are trends in authorship and useage over time. Authors came from Australia (73%, represented in 15 of 15 years), the Americas (Canada, USA and South American countries) (12%, represented in 13 of 15 years), New Zealand (8%, represented in 14 of 15 years), other Pacific and Asian countries (4%, represented in 11 of 15 years) and Europe (2%, represented in 11 of 15 years). Overall, 46% of authors were academics. Using the Scopus database in April 2008 and the cited reference feature in the ISI Web of Science in July 2008, =84% papers published each year between 1993 and 2001 were cited at least once in each database, declining to under 19% in 2007 because articles had far less time to accrue citations. Using the cited reference feature in the ISI Web of Science database in July 2008, authors citing Pacific Conservation Biology came from Australia and 82 other countries. Compared to 24 journals listed in Thomson Reuters? ?Biodiversity Conservation? category in 2008, Pacific Conservation Biology ranked between the 10th and 39th percentiles for a range of citation statistics derived from both Scopus and ISI Web of Science, including: Journal Impact Factor (JIF) for 2006, mean JIF for 2001?2006 and h-index, g-index, mean citations/paper and median citations/paper for 2000?2006. Overall, most authors are Australian, but with consistent international representation and academic and non-academic authors. With time, most papers are cited (including many international citations) and citation statistics are within the range of similar journals abstracted in ISI Web of Science. On the basis of the results, we offer suggestions for increasing Pacific Conservation Biology?s use and a critique of the growing tendency to accept citation-based bibliometric data as indicators of the quality or achievements of journals and individual scientists.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".