Bibliographic record
Abstract
Rivalries in the workplace can be destructive to both personal career growth and group success. Many attempts to reverse rivalries fail because of the complex way emotion and reason operate in the building of trust. Using a method called the 3Rs, an effective leader can turn a rival into a collaborator, setting the stage for a healthy work life while driving fresh thinking within an organization. Step 1 of the method is redirection, shifting a rival's negative emotions away from the adversarial relationship. This creates an opening for Step 2, reciprocity, through which a relationship can be established. Here, the essential principle is to give before you ask--offering a rival something of clear benefit and "priming the pump" for a future return that requires little effort on the rival's part. Step 3, rationality, sets expectations of the new relationship so that efforts made using the previous steps don't come off as disingenuous. A rival is encouraged to see collaborative opportunities from a reasoned standpoint. A key advantage of the 3Rs is that the method can work to reverse all kinds of rivalries, including those with subordinates, peers, and superiors.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.037 |
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".