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
The 2006 volume of Economic Inquiry consisted of 52 papers in a wide variety of fields. These papers included theoretical, empirical, and experimental studies, and were in fields that ranged from labor and industrial organization to macroeconomics, growth, public choice, and health economics. We report data on editorial activities in two ways. Table I is organized by the year in which an event (submission, acceptance, rejection) occurred, and Table II is organized by the year in which a paper was originally submitted. These tables start with data from 1997, the year that Texas A&M University became the editorial home of Economic Inquiry, and continue through the time of this writing, which is early June 2007. Table I shows that our submissions in 2006 numbered 259, maintaining the level increase we experienced in 2005 and significantly higher than the annual submissions from 1997 through 2004. We accepted 69 papers in 2006. Our acceptance rate in 2006, counting papers accepted in 2006 no matter when submitted, was 29 percent, at the high end of our experience since 1997. We also rejected 175 papers in 2006. Table II provides a better perspective on acceptance and rejection rates. This table is organized by year in which a paper was submitted. Table II also takes into account the outstanding revisions in progress. In many years prior to 2006 there were papers submitted but on which there has not yet been a final decision. For example, there was 1 paper submitted in 2003 that are still in the revise and resubmit category in 2006. Data in Table II can be used to determine the ratio of reject decisions to non-reject decisions by year of submission. For example, in 1997 we rejected 2.5 papers for every paper we either accepted or gave an invitation to revise and resubmit. In 2002 we rejected 152 papers, accepted 60, and there are no papers in the revise and resubmit process, so the ratio of reject decisions to non-reject decisions (defined as accepted or revise and resubmit) currently stands at 2.5. In 2003 this ratio was 2.4, was 2.3 in 2004, and 2.5 in 2005. Another important measure of editorial performance relevant to authors is the speed with which decisions are made. This is commonly reported as the average time to the first decision. Table III reports this statistic for Economic Inquiry over 1997--present. We continue to keep this number to near five months in 2005. Table III also reports the average time from submission to acceptance, a statistic recommended by Glenn Ellison of MIT. This statistic is reported in Table III for Economic Inquiry for the years 1997--present. Our time to first decision averaged 5.0 months, continuing a slight downward trend since 2003, and at the lower end of our 1997-2006 experience. In 2006 Economic Inquiry's time between submission and final acceptance increased to 15.9 months. (In comparison, Ellison reported that in 1999 the average number of months between initial submission and acceptance was 19.8 for eight general interest journals (other than Economic Inquiry), including 20.3 months for the Journal of Political Economy, 18.2 months for the Economic Journal, and 13.0 months for Quarterly Journal of Economics. In 1999 Economic Inquiry achieved a time of 11.6 months from original submission to final acceptance, among the fastest of the general interest journals for which Ellison obtained data.) The editorial team for calendar year 2006 consisted of Michael McKee of the University of Calgary, Ron Oaxaca of the University of Arizona, and Amy Glass and Thomas Saving, both of Texas A&M University. Amy Glass joined the team in September 2006. I should note that Ron Oaxaca has resigned in early 2007, although he continues to handle revisions in progress, and Timothy Gronberg of Texas A&M University joined our editorial team in early 2007. I thank all our co-editors for their dedication and hard work. I also thank Jennifer Broaddus, our editorial assistant and office manager, the staff at Blackwell Publishing and the staff at the Western Economic Association International. …
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.
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 | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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