Investment: Building for the Future
Bibliographic record
Abstract
Examples Investment situations are extended situations in which each person, at each of a series of preliminary steps, must make an “investment” in order to move toward a desirable goal. For example, in seeking to establish a successful business, the owners proceed through a series of preliminary steps at which they invest time and effort in order to reach their goal. They work evenings and weekends, forgo short-term financial profits, and reinvest early earnings in development activities, including employee training and construction projects. In the situations considered here, those of mutual investment, both owners must engage in these activities. If either loses heart and backs out of the venture, their investments are lost and the company fails. If both persist and jointly work their way through the early steps, making suitable choices and effective investments of time, effort, and resources, they may achieve a desirable goal – a company that will earn substantial profits. In like manner, the partners in an emerging romantic relationship must work together, making their way through preliminary steps at which they invest a variety of resources in their involvement. They disclose private thoughts and feelings to each other, purchase joint possessions, develop a shared friendship network, spend time becoming acquainted with one another's family members, and exert effort to resolve conflicts involving incompatible preferences. If both partners successfully surmount the preliminary hurdles of their involvement, they may achieve a desirable goal – a committed, trusting relationship in which each partner gratifies the other's needs.
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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 | 0.001 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".