"Economia Politica” over the last 25 years, and more:a snapshot of contents and scholars
Bibliographic record
Abstract
A quarter of a century is a remarkable outcome for a scientific journal. On the one hand, the time-span is long enough to map the co-evolution of its discipline and of its editorial context, and to enrich the debate of the history of science. On the other hand, the mass of its articles is large enough to identify the structural characteristics of its typical contents, the general profile of its representative authors, and to orient eventual submission decisions. With these two aims, the paper presents a “snapshot” of the last 27 years of the Italian economics journal Economia Politica. Looking at the JEL (Journal of Economic Literature) codes of its articles and at the bibliographic info of its authors, some distinguishing features are identified, along with some structural changes occurred along its history. The journal distinguishes as a “generalist” one (as opposed to “specialist” journals), with a remarkable distribution of subjects, but with some specializations too. In the last ten years, it has undergone some important changes. In particular, a substantial boost of internationalisation (not only in terms of publication languages, but also in terms of authorships), an increasing weight of co-authorships (and of non academic authorships, though to a lesser extent), a widening of the topics coverage, an increased specialisation on the growth and development implications of technological change, and a new focus on cutting-edge issues of industrial organization (e.g. firm strategy and organization). In terms of authorships, the journal distinguishes for a balanced ratio between senior professors (or equivalent) and junior researchers (or equivalent), towards whose contributions it shows a substantial propensity. In spite of the long editorial history of a group of Italian academics, the distribution of the authors’ affiliations is far from concentrated. The distribution of their faculties is instead dominated by those of economics, but with relevant shares of other surrounding disciplines, such as political sciences and law.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".