Beyond the Code: Understanding Professional Users' Perspectives on AI Implementation
Bibliographic record
Abstract
While Artificial Intelligence (AI) has the potential to amplify economic productivity, and reduce expenses, it concurrently triggers investigations into the socially acceptable boundaries of its implementation.This study aims to promote a deeper comprehension of this issue by elucidating a better comprehension of actual AI usage for the professional proposes, the perceptions, the main risks, and the expectations for future usage on the lens of the professional users.The research commences with a descriptive and literature-driven exploration.Subsequently, it the foundational phase conducts an exploratory investigation via semi-structured interviews to managers and analysts who utilizes AI for professional purposes.Differentiation in usage was observed based on each participant's specific needs, ranging from text improvement to decision-making and programming.This study revealed patterns of usage, such as: perceptions, expectations, evaluations of AI's potential, as well as associated risks and concerns.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".