Bibliographic record
Abstract
Ryan Stevens is staring down a decision that could define his legacy. At the helm of AFT Capital’s inaugural fund, he’s got one shot left—one final investment that could determine not just the fund’s performance, but his future in venture capital. The stakes? Immense. With investors watching closely, and with AFT’s DEI promises etched into its identity, Ryan is caught in a high-stakes tug-of-war. SecureChain looks like a grand slam—technologically robust, market-ready, and led by a proven team. But its leadership lacks diversity, a detail that’s impossible to ignore. The two alternative startups boast minority founders and strong DEI alignment, yet their financial trajectories are riskier. Ryan is wrestling with the hardest question of his career: does he chase the clearest path to returns—or invest in the change he claims to believe in?
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".