What Are We Talking About? Natural Language Processing in Organisations
Bibliographic record
Abstract
This symposium brings together experts in natural language processing to demonstrate state-of-the-art applications of for analysing text within organisations. Recent innovations are used to understand fundamental topics for managers of the future - teamwork, leadership communication, institutional change, cultural diffusion, and diversity. The presenters will show how a modern toolkit for text analysis can provide innovative solutions to some of the most important problems in our field. A Flexible Python-Based Toolkit for Analyzing Team Communication Author: Xinlan Emily Hu; Wharton How Leaders Build Trust in High-Stakes Communications. Author: Michael Yeomans; Imperial College Business School Author: Evita Huai-ching Liu; Bocconi U. From the Boardroom to the Bedroom: The Expansion and Abstraction of Management as a Cultural Logic Author: Ziwen Chen; Stanford Graduate School of Business Author: Douglas Guilbeault; U. of Pennsylvania Author: Amir Goldberg; Stanford U. Racial Polarization of Who Sponsors Civil Rights Legislation in the United States Author: Joshua Jackson; Northwestern Kellogg School of Management Author: Nour Kteily; Northwestern Kellogg School of Management Intersectional Gender, Race, and Class Stereotyping: Tests in Contemporary and Historical Naturalist Author: Tessa Charlesworth; Northwestern Kellogg School of Management Author: Mazarin Banaji; Harvard U. Author: Aylin Caliskan; U. of Washington Author: Kshitish Ghate; Carnegie Mellon U. Author: Gandalf Nicolas; Rutgers U., New Brunswick
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.023 |
| 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".