Bibliographic record
Abstract
The Journal of Transport and Land Use enters its fourth year with this issue. We are pleased to see how far it has come. As an open access journal, we are doing something that remains in some ways experimental, but in others quite proven. Our readership is large and growing, in one year (December 1, 2009 to December 1, 2010) we had 11,759 visits from 7,714 visitors, coming from 119 countries. While the plurality are from the US, it is not a majority. Other large readerships come from the United Kingdom, Canada, India, the Netherlands, Italy, Australia, China, Indonesia, and Japan. In three years (through Fall 2010) we had 231 submissions and 37 published articles (excluding 15 book reviews, issue introductions, discussions, etc.). Our acceptance rate is 28 percent. We are indexed in Google Scholar and Research Papers in Economics (RePEc), and have applied to other online indexing services, though these take time. According to Google Scholar, JTLU articles have been cited over 184 times, or 3.53 citations per item, with an H-index of 6 (6 articles cited 6 or more times).
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.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.167 | 0.093 |
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".