Theory‐Driven Perspectives on Generative Artificial Intelligence in Business and Management
Bibliographic record
Abstract
The etymology of words is often a source of insights to not only make sense of their meaning, but also speculate and imagine meanings that are not so obvious and thereby see the phenomena signalled by these words in new and surprising ways.The etymology of 'artificial' and 'intelligence' does not disappoint.'Artificial' comes from 'art' and -fex 'maker', from facere 'to do, make'.'Intelligence' comes from inter 'between' and legere 'choose, pick out, read' but also 'collect, gather'.There is enough in these etymologies to offer a few speculations and imagine the contours of generative artificial intelligence (GAI) and its possible futures.The first of these is inspired by the craft of making and relates to the very function and use of AI.Most of the current fascinations with AI emphasize the predictive capacity of the various tools increasingly available and at easy disposal.Indeed, marketers know well in advance when we will need the next toothbrush, fuel our cars, buy new clothes, and so forth.The list is long.This feature of AI enchants us when, for instance, one thinks of a product and, invariably, an advertisement related to that product appears on our social media page.This quasi-magical predictive ability captures collective imaginations and draws upon very well-ingrained forms of knowledge production which presuppose that data techniques are there to represent the world, paradoxically, even when it is not there, as is the case with predictions.The issue is that the future is not out there; we do not know what future generations want from us and still, we are increasingly called to respond to their demands.Despite the availability of huge amounts of data points and intelligence, the future, even if proximal and mundane -as our examples above, always holds surprises.This means that AI may be useful not to predict the future, but to actually imagine and make it, as the -fex in 'artificial' reveals.This is the art in the 'artificial' and points to the possibility of conceiving AI as a compositional art, which helps us to create images of the future, sparks imagination and creativity and, hopefully, offers a space for speculation and reflection.The word intelligence is our second cue, which stresses how 'inter' means to be and explore what is 'in between'.As entrepreneurs are in between different ventures and explore what is not yet there (Hjorth and Holt, 2022), AI may be useful to probe grey areas between statuses
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".