Can student managed investment funds (SMIFs) narrow the environmental, social and governance (ESG) skills gap?
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold. First, it endeavors to document the current state of environmental, social and governance (ESG) pedagogy within undergraduate finance courses of business schools, and second, it seeks to show how business schools can leverage student managed investment funds (SMIFs) to swiftly integrate ESG pedagogy. Design/methodology/approach The study is comprised of two sections that use different methodologies. The first part of the study involves a manual content analysis of undergraduate finance course textbooks, and related instructor materials are used to estimate the average coverage of ESG-related topics. Next, a case study of a SMIF that has recently integrated an ESG framework is provided to illustrate how this pedagogical innovation is effective in teaching ESG skills. Findings The findings of the content analysis of the three most commonly used textbooks in a sample of 17 Canadian universities, as well as associated instructor material, provide evidence that the primary emphasis in traditional curriculum remains on the shareholder, with little attention paid to ESG factors. The case study of an existing SMIF clearly demonstrates how a student-led development of an ESG framework provides the setting for effective, experiential learning. Originality/value This study shows that while traditional teaching settings, like lectures, may be slow to adapt to the rapidly changing needs of industry, nontraditional teaching venues, such as SMIFs, can be leveraged to meet industry demand for ESG skills, thereby closing the skills gap, enhancing student employability and increasing the relevance of business school education.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".