Knowings That Matter: Recognizing and Including Organizational Knowledge in AI Development
Bibliographic record
Abstract
Artificial intelligence (AI) technologies differ from previous organizational technologies in their ability to autonomously engage in complex knowledge work. Crucial to this capability is the incorporation of specialized knowledge during development. However, developing early instances AI for complex cross-boundary work presents challenges in determining essential knowledge and how it should manifest in the technology's features and usage behaviors. To explore this, I conducted a two-year qualitative field study on the concurrent design and development of AI-based scheduling support tools for Operating Rooms (ORs) at two hospitals. A single technology vendor collaborated with teams from each hospital, adapting the same early prototype to local conditions. Based on 96 hours of observation and 77 interviews, I identified how local differences in knowledge perceptions and arrangements can drive divergences in the technology's development path. This study contributes to a processual ontology of artificial intelligence by showing that failing to recognize and include all clinical and processual knowings associated with OR scheduling results in an assembly of somewhat successful technological elements that do not ultimately cohere into an effective AI system capable of participating in complex cross-boundary healthcare processes.
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 | 0.038 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".