Bibliographic record
Abstract
Purpose The purpose of this paper is to describe how a student-managed investment fund (SMIF) moved from an idea to an operational program over the period of a year at Memorial University in Newfoundland, Canada. The aim is to provide insight to other institutions on how to build capacity when developing their own SMIF. Design/methodology/approach I summarize the choices made with respect to funding source, governance structure, faculty involvement, recruitment, investment activities and integration into curriculum. Findings Underlying these choices were challenges pertaining to capacity, student competencies, the existing finance program and ties to industry. Through the development of the SMIF, efforts ensured that capacity was suitably developed in each of these areas. Research limitations/implications This paper provides insight to other institutions on how to build capacity while developing their own SMIF. Practical implications This account provides the field with a unique perspective. It is written following a year spent developing a SMIF that is about to launch. Originality/value This account provides the field with a unique perspective. It is written by a new faculty member following a year spent developing a SMIF that is about to launch.
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 | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".