Bibliographic record
Abstract
It has now been two years since we assumed the positions of co-editors of Multinational Business Review (MBR).In our first editorial, we promised to sustain the MBR tradition of being a leader in "macro" IB and committed the journal to maintaining its reputation as a home for new ideas and thoughtful perspectives on current issues and controversies.At the time, we noted the first volume, published in 1993, contained papers on NAFTA, the prospects for a single European market, and political risks facing US firms abroad.Plus ca change.We also noted at the time that that MBR was included in the Emerging Sources Citation Index (ESCI), a step towards inclusion the Social Science Citation Index (SSCI).We are proud to announce that MBR has now been accepted for inclusion the SSCI, and will receive its first impact factor in June 2019.This is of course good news that will enhance our reputation and will increase the number and quality of submissions.We wish to express our gratitude to our editorial reviewer board members, authors, reviewers and readers, whose collective efforts contributed to this outcome.We will need your assistance more than ever to manage the anticipated increase in submissions and look forward to working with you all to continue to make MBR a home for innovative and thoughtful ideas.As promised in our editorial two years ago, we have worked hard to maintain the original MBR spirit of directly and immediately addressing important issues, while taking steps to improve the ways in which this is done.Since the first issue of 2016, we have published one perspective paper per issue and will continue to do so.The authors of these perspective papers include
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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.086 | 0.074 |
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".