Language-Based Artificial Intelligence and Organizational Knowing
Bibliographic record
Abstract
This paper theorizes how language-based artificial intelligence (AI), especially large language models (LLM), may shape the nature of organizational knowing and particularly knowledge integration. Drawing on the concept of language games, we articulate what is conceptually novel about recent advances in language-based AI. Language-based AI can emulate context-aware linguistic practices and thereby translate and apply knowledge across linguistic communities. We elaborate how these new capabilities facilitate de-personification of knowledge and effective translation of knowledge across expert groups. We link these two processes to the contraction of organizational boundaries, the polarization of knowledge work between specialists and generalists, dynamics of innovation, and the erosion of traditional knowledge integration mechanisms, such as shared organizational identity and social networks. We conclude by discussing the broader implications of our arguments for the study of language-based AI technologies, organization-level impacts of AI, and the knowledge-based view of the firm. Overall, our paper seeks to draw attention to and facilitate the analysis of the broader organizational implications of AI beyond its effects on the individual knowledge worker.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".