Contextual Limits of Incorporating Artificial Intelligence in an Organization
Bibliographic record
Abstract
Artificial intelligence (AI) is being presented as a Holy Grail with the capacity to do things that were considered science fiction some years ago. We argue in this piece that within organizational settings, there are contextual limits to what we can currently expect AI to achieve. Building on a 5-year long ethnographic study of a case company’s AI journey, we outline two contextual considerations for incorporating AI in an organization – unique vs. recurring instances and tangential vs. overlapping scopes. With these findings, our study refocuses attention on context as an essential component in theorizing about AI in contemporary organizational settings. The study shifts the narrative about AI to a contextual sensitivity in which the specificity of an AI technology is considered salient within a situated context. We outline implications for future studies in considering the appropriateness of describing AI with a one-size-fits-all all narrative.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".