Bibliographic record
Abstract
In this interview Robert Sutton talks about some of the counterintuitive practices he believes spur innovation. He proposes that companies should adopt eleven practices. Hire slow learners (of the organizational code). Hire people who make you uncomfortable, even those you dislike. Hire people you probably do not need. Use job interviews to get ideas not just to screen candidates. Encourage people to ignore and defy superiors and peers. Find some happy people and get them to fight. Reward success and failure and punish inaction. Decide to do something that will probably fail, and then convince yourself and everyone else that success is certain. Think of some ridiculous or impractical things to do, and then plan to do them. Avoid, distract, and bore customers, critics and anyone else who just wants to talk about money. Do not try to learn anything from people who say they have solved the problems you face. Forget the past, especially your company’s successes. In sum, he believes that creative companies and teams are inefficient and annoying places to work.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".