Rankings of Academic Journals and Institutions in Economics
Why this work is in the frame
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Bibliographic record
Abstract
There has been a lot of recent research literature on rankings of economics \ndepartments throughout the world. They serve as signals tools for attracting new faculty and retaining older in highly ranked institutions and also help attract the best graduate students who have academic aspirations. Many times these rankings are used by university administrators to allocate scarce education funds to di¤erent departments according to their success in these rankings. There has been a long standing tradition for US economic departments to be ranked (see Scott and Mitias (1996) and Dusansky and Vernon (1998) for recent such rankings). Recent European studies of this kind include Kirman and Dahl \n(1994) and Kalaitzidakis, Mamuneas and Stengos (1999). There have been also \nrankings of departments in Asia (see Jin and Yau (1999)), Canada (see Lucas (1995)), as well as Australia (see Harris (1990)). Rankings are also constructed in other related disciplines such as finance for the same reasons outlined above, see Chung and Cox, (1990)). Coupé (2000) provides a comprehensive ranking of economic departments \nworld-wide. His ranking methodology is based on employing various performance measures from the existing literature, such as the citations weighted journal ranking by Laband and Piette (1994), to assess the output of individual researchers and then according to their a¢liation compute the department \nrankings. He reports the rankings from the different methodologies and he also \npresents a ranking based on the average of these different methods. However, the latter ranking is based on averaging rank statistics and as such it is not very \ninformative. A common drawback that permeates most of the studies that produce department rankings is that they are based on a certain ranking of economic journals that was itself constructed over a certain time period that typically is different from the corresponding period of the department rankings. Hence, a typical list of journals that is citations weighted uses weights that correspond to an earlier period from the current one. That means that the most current \nresearch outlets that are used by the profession (new journals, improved older \njournals etc.) are not used with their true weights for the period under investigation. \nHence, potentially rankings that use a list of research journals with weights from a di¤erent period may produce biased and unreliable rankings for the current period. In this paper we try to rectify this defficiency in the literature by both computing an updated list of journal rankings with current weights computed from their citations impact and then use those to produce a world wide ranking of academic institutions. \nThe paper is organized as follows. The next section provides the methodology that we employ to arrive at the new journal rankings. We provide details of the way that we arrive at these journal rankings that form the weights to be used for the derivation of the institutional rankings as well as the methodology that is used to construct the latter. In the next section we discuss the results. Finally we conclude.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it