What Are We Talking About? Natural Language Processing in Organisations
Bibliographic record
Abstract
All four talks use state-of-the-art natural language processing tools to understand fundemental functions within organisations. The research covers a variety of essentially linguistic tasks - trust-building, job interviews, leadship speeches, and moral judgment. All papers show what is possible when organisatgions use modern tools for storing and analyzing text data to understand the social interactions within them. How Evaluations are Shaped through Interaction: Evidence from Software Engineering Interviews Author: David Holtz; Author: Sanaz Mobasseri; UCL School of Management Author: Joe Zhang; Stanford Graduate School of Business Author: Janet Xu; Stanford University Rich Talk: How Communication Predicts Cooperative Behavior Author: Xinlan Emily Hu; Wharton School Author: Mohammed Alsobay; Author: Jared R. Curhan; Author: Abdullah Almaatouq; Projecting Agency: Language Expression of Self-Presented Agency in Organizational Communication Author: Yutao Chen; Author: Oscar Stuhler; Northwestern University Partisans misperceive outgroup members’ motives for expressing moral outrage Author: William Brady; Kellogg School of Management, Northwestern University Author: Chen-Wei Yu; Northwestern University
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".