RAUSP Management Journal: past, present, and future challenges. [Editorial]
Bibliographic record
Abstract
Despite being the oldest Brazilian journal in Management, RAUSP Management Journal has undergone several changes in the past few years.Since 2016, we have increasingly developed our internationalisation process (Saes & Hourneaux Junior, 2019) by publishing articles in the English language and being more present in an international context, as acknowledged by both authors and institutions (Hourneaux Junior & Hamza, 2022).These changes have brought positive and rewarding consequences to our journal, as described in the next section.Nevertheless, despite the good results we have achieved so far, there are still several critical challenges to be faced, which will be addressed in the final section. About RAUSP Management Journal's performanceWe have seen an evolution in all the indexes and impact factor calculations in which our journal is included.Table 1 presents a summary of these figures in recent years.In all those indexes, our performance has expressively improved.In the past few years, besides the evident progress in the aspects mentioned above, RAUSP Management Journal has been included in both Scopus (Elsevier) and Emerging Sources Citation Index (Clarivate Analytics) databases, essential references for a journal's acknowledgement in the academic community.Another relevant achievement was the inclusion of our journal in the Chartered Association of Business Schools by the Academic Journal Guide 2021.Since 2019, the number of article submissions from overseas authors has been increasing.As a result, the rate of international versus Brazilian authors is getting more balanced.While in 2019, 70% of the overall submissions to RAUSP Management Journal were from Brazilian authors, in 2022, this number declined to 52%. Figure 1 shows the countries of submitting papers' authors in 2022.Table 1.
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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".