Personal Futures: A Pathway for a New Generation of Investors
Bibliographic record
Abstract
The world of investing can be overwhelming and even intimidating, for the new investor. Uncertainties abound in speculating pundits, media given to hyperbole, multiple sources of advice, and even one’s own knowledge the markets or rather, lack thereof. Considering this current state, methods to increase investor confidence in both themselves and in their understanding of the markets are sought-after and desired, by new investors and experienced alike, to potentially provide the impetus to invest, and the resiliency to continue. Whereas investing is inherently concerned with the future, personal and strategic foresight are similarly applied with a future-focused lens. This paper explores the \nintersections of financial planning, market futures, and foresight (both personal and strategic) to answer the questions: How can non-professional investors employ personal and strategic foresight to sustain confidence in an uncertain future? Expert interviews and an exploratory workshop were conducted, informed by a review of key aspects of the investing world, investors themselves, and personal and \nstrategic foresight methods, to propose avenues for foresight in investing, and a personal foresight investing model.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".