The Misalignment of the FT50 with the Achievement of the UN’s SDGs: A Call for Responsible Research Assessment by Business Schools
Bibliographic record
Abstract
Publication in the list of 50 journals endorsed by the Financial Times (i.e., the FT50) has become ‘institutionalized’ as a primary measure of research quality and prestige by business schools and faculty. This study investigated the extent to which this closed publication system is (mis)aligned with societal imperatives, in particular the United Nation’s 17 Sustainable Development Goals (SDGs). Research methods included both inductive and deductive analysis. Undergraduate and graduate student research assistants, enrolled in business-related programs, read all 4522 articles published by FT50 journals in 2019 and assessed their relevance to explicit and implicit concepts in the SDG framework. Additionally, potential biases that might stifle research innovation in support of the SDGs were explored. Findings included that 90% of articles were found to have no ‘explicit’ relationship to the SDGs, while only 17% were interpreted as having an implicit relationship. SDG-related articles were disproportionately from one journal-the Journal of Business Ethics (48.1%). There was also an over-representation of observed white male primary authors, who used North American (NA) data sets from NA institutions. A logistic regression model determined that the predicted probability of an SDG-related article increased with observed female primary authors, who used non-NA data sets and institutions. The next steps include comparing this methodological approach with machine learning techniques to find a more efficient and robust method for analyzing an article’s SDG content. Business Schools with sustainability as a core value are encouraged to move beyond FT50 publications for assessing research quality, including for tenure and promotion purposes, and place more focus on assessing research relevance and impact.
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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 | 0.473 | 0.769 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".