Bibliographic record
Abstract
Somewhere there's a 26-year-old out trying to destroy your business." 1 I.A CLASSROOM EXPERIMENT Recently, I conducted an experiment.Prompted by a student who had established a new joint law school-business school student group at the University of Southern California, I ran an extracurricular case study session in which J.D. and M.B.A. students worked together to find a solution for a real company facing a very real business challenge.The case involved a startup in Toronto called Innovation Exchange 2 ("IX"), an online open innovation platform connecting sponsor companies posting innovation challenges (for example, a new type of yogurt container or a new banking product) with a community of innovators.3 Innovation Exchange was encountering the following problem: In a significant number of cases, getting potential sponsor companies to sign up was slowed down or even halted by the complexity and legalese of their contract.As CEO Stephen Benson put the challenge, "It would be great if we could get this thing down to a page or two of easily readable content that the nonlawyer Chief Innovation Officer of a potential sponsor company could easily understand and communicate internally."Copyright 2011 by Gillian K. Hadfield.* Many thanks to my J.D. students in Advanced Contracts at USC and to the students
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 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".