Student Managed Investment Funds: Experiences of a Fund at a UK University
Bibliographic record
Abstract
First founded in 1950, Student Managed Investment Funds (SMIFs) have spread widely across the US and Canada with over 400 now documented. SMIFs have taken longer to develop elsewhere. In Europe SMIFs are most popular in the UK with about 30 funds known to the authors. This paper adds to the available literature by documenting the operation of UK SMIFs and details the differences between these funds and typical North American SMIFs. In addition we describe and evaluate the foundation and operations of the Griff Fund, run by students at a university in the North of England, and one of the oldest funds in the UK. Our description of the Fund provides resources and guidance for others who might seek to establish such a fund. Our evaluation considers the strengths and benefits of student partnership and co-creation but also examines the challenges faced through the lens of student equity and inclusion.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".